Merge branch 'develop' into pr/Axel-CH/8779

.github/dependabot.yml

@@ -10,7 +10,7 @@ updates:
   directory: "/"
   schedule:
     interval: weekly
-  open-pull-requests-limit: 10
+  open-pull-requests-limit: 15
   target-branch: develop
 
 - package-ecosystem: "github-actions"

.github/workflows/ci.yml

@@ -461,7 +461,7 @@ jobs:
         python setup.py sdist bdist_wheel
 
     - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@v1.8.8
+      uses: pypa/gh-action-pypi-publish@v1.8.10
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -469,7 +469,7 @@ jobs:
         repository_url: https://test.pypi.org/legacy/
 
     - name: Publish to PyPI
-      uses: pypa/gh-action-pypi-publish@v1.8.8
+      uses: pypa/gh-action-pypi-publish@v1.8.10
       if: (github.event_name == 'release')
       with:
         user: __token__

.pre-commit-config.yaml

@@ -8,7 +8,7 @@ repos:
         # stages: [push]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.3.0"
+    rev: "v1.5.0"
     hooks:
       - id: mypy
         exclude: build_helpers
@@ -18,7 +18,7 @@ repos:
           - types-requests==2.31.0.2
           - types-tabulate==0.9.0.3
           - types-python-dateutil==2.8.19.14
-          - SQLAlchemy==2.0.19
+          - SQLAlchemy==2.0.20
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort

.readthedocs.yml

@@ -1,8 +1,14 @@
 # .readthedocs.yml
+version: 2
 
 build:
-  image: latest
+  os: "ubuntu-22.04"
+  tools:
+    python: "3.11"
 
 python:
-  version: 3.8
-  setup_py_install: false
+  install:
+    - requirements: docs/requirements-docs.txt
+
+mkdocs:
+  configuration: mkdocs.yml

build_helpers/install_ta-lib.sh

@@ -8,8 +8,9 @@ if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
   tar zxvf ta-lib-0.4.0-src.tar.gz
   cd ta-lib \
   && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
-  && curl 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.guess' -o config.guess \
-  && curl 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.sub' -o config.sub \
+  && echo "Downloading gcc config.guess and config.sub" \
+  && curl -s 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.guess' -o config.guess \
+  && curl -s 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.sub' -o config.sub \
   && ./configure --prefix=${INSTALL_LOC}/ \
   && make
   if [ $? -ne 0 ]; then

build_helpers/install_windows.ps1

@@ -5,7 +5,7 @@ python -m pip install --upgrade pip wheel
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
 
-pip install --find-links=build_helpers\ TA-Lib
+pip install --find-links=build_helpers\ --prefer-binary  TA-Lib
 
 pip install -r requirements-dev.txt
 pip install -e .

config_examples/config_full.example.json

@@ -89,7 +89,6 @@
     ],
     "exchange": {
         "name": "binance",
-        "sandbox": false,
         "key": "your_exchange_key",
         "secret": "your_exchange_secret",
         "password": "",

docs/bot-basics.md

@@ -7,7 +7,7 @@ This page provides you some basic concepts on how Freqtrade works and operates.
 * **Strategy**: Your trading strategy, telling the bot what to do.
 * **Trade**: Open position.
 * **Open Order**: Order which is currently placed on the exchange, and is not yet complete.
-* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. XRP/USDT).
+* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. `XRP/USDT` for spot, `XRP/USDT:USDT` for futures).
 * **Timeframe**: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
 * **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
 * **Limit order**: Limit orders which execute at the defined limit price or better.
@@ -20,6 +20,20 @@ This page provides you some basic concepts on how Freqtrade works and operates.
 
 All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt / Dry-run modes, the exchange default fee is used (lowest tier on the exchange). For live operations, fees are used as applied by the exchange (this includes BNB rebates etc.).
 
+## Pair naming
+
+Freqtrade follows the [ccxt naming convention](https://docs.ccxt.com/#/README?id=consistency-of-base-and-quote-currencies) for currencies.
+Using the wrong naming convention in the wrong market will usually result in the bot not recognizing the pair, usually resulting in errors like "this pair is not available".
+
+### Spot pair naming
+
+For spot pairs, naming will be `base/quote` (e.g. `ETH/USDT`).
+
+### Futures pair naming
+
+For futures pairs, naming will be `base/quote:settle` (e.g. `ETH/USDT:USDT`).
+
+
 ## Bot execution logic
 
 Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.

docs/bot-usage.md

@@ -3,7 +3,7 @@
 This page explains the different parameters of the bot and how to run it.
 
 !!! Note
-    If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
+    If you've used `setup.sh`, don't forget to activate your virtual environment (`source .venv/bin/activate`) before running freqtrade commands.
 
 !!! Warning "Up-to-date clock"
     The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.

docs/configuration.md

@@ -188,7 +188,6 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
 | | **Exchange**
 | `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
-| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
 | `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String

docs/data-analysis.md

@@ -27,7 +27,7 @@ For this to work, first activate your virtual environment and run the following
 
 ``` bash
 # Activate virtual environment
-source .env/bin/activate
+source .venv/bin/activate
 
 pip install ipykernel
 ipython kernel install --user --name=freqtrade

docs/developer.md

@@ -77,7 +77,7 @@ def test_method_to_test(caplog):
 
 ### Debug configuration
 
-To debug freqtrade, we recommend VSCode with the following launch configuration (located in `.vscode/launch.json`).
+To debug freqtrade, we recommend VSCode (with the Python extension) with the following launch configuration (located in `.vscode/launch.json`).
 Details will obviously vary between setups - but this should work to get you started.
 
 ``` json
@@ -102,6 +102,19 @@ This method can also be used to debug a strategy, by setting the breakpoints wit
 
 A similar setup can also be taken for Pycharm - using `freqtrade` as module name, and setting the command line arguments as "parameters".
 
+??? Tip "Correct venv usage"
+    When using a virtual environment (which you should), make sure that your Editor is using the correct virtual environment to avoid problems or "unknown import" errors.
+
+    #### Vscode
+
+    You can select the correct environment in VSCode with the command "Python: Select Interpreter" - which will show you environments the extension detected.
+    If your environment has not been detected, you can also pick a path manually.
+
+    #### Pycharm
+
+    In pycharm, you can select the appropriate Environment in the "Run/Debug Configurations" window.
+    ![Pycharm debug configuration](assets/pycharm_debug.png)
+
 !!! Note "Startup directory"
     This assumes that you have the repository checked out, and the editor is started at the repository root level (so setup.py is at the top level of your repository).
 

docs/faq.md

@@ -36,7 +36,7 @@ Running the bot with `freqtrade trade --config config.json` shows the output `fr
 This could be caused by the following reasons:
 
 * The virtual environment is not active.
-  * Run `source .env/bin/activate` to activate the virtual environment.
+  * Run `source .venv/bin/activate` to activate the virtual environment.
 * The installation did not complete successfully.
   * Please check the [Installation documentation](installation.md).
 

docs/freqai-parameter-table.md

@@ -100,12 +100,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 
 #### trainer_kwargs
 
-|  Parameter | Description |
-|------------|-------------|
-|  |  **Model training parameters within the `freqai.model_training_parameters.model_kwargs` sub dictionary**
-| `max_iters` | The number of training iterations to run. iteration here refers to the number of times we call self.optimizer.step(). used to calculate n_epochs. <br> **Datatype:** int. <br> Default: `100`.
-| `batch_size` | The size of the batches to use during training.. <br> **Datatype:** int. <br> Default: `64`.
-| `max_n_eval_batches` | The maximum number batches to use for evaluation.. <br> **Datatype:** int, optional. <br> Default: `None`.
+| Parameter    | Description |
+|--------------|-------------|
+|              |  **Model training parameters within the `freqai.model_training_parameters.model_kwargs` sub dictionary**
+| `n_epochs`   | The `n_epochs` parameter is a crucial setting in the PyTorch training loop that determines the number of times the entire training dataset will be used to update the model's parameters. An epoch represents one full pass through the entire training dataset. Overrides `n_steps`. Either `n_epochs` or `n_steps` must be set. <br><br> **Datatype:** int. optional. <br> Default: `10`.
+| `n_steps`    | An alternative way of setting `n_epochs` -  the number of training iterations to run. Iteration here refer to the number of times we call `optimizer.step()`. Ignored if `n_epochs` is set. A simplified version of the function: <br><br> n_epochs = n_steps / (n_obs / batch_size) <br><br> The motivation here is that `n_steps` is easier to optimize and keep stable across different n_obs - the number of data points.  <br> <br> **Datatype:** int. optional. <br> Default: `None`.
+| `batch_size` | The size of the batches to use during training. <br><br> **Datatype:** int. <br> Default: `64`.
 
 
 ### Additional parameters

docs/freqai-reinforcement-learning.md

@@ -20,7 +20,7 @@ With the current framework, we aim to expose the training environment via the co
 
 We envision the majority of users focusing their effort on creative design of the `calculate_reward()` function [details here](#creating-a-custom-reward-function), while leaving the rest of the environment untouched. Other users may not touch the environment at all, and they will only play with the configuration settings and the powerful feature engineering that already exists in FreqAI. Meanwhile, we enable advanced users to create their own model classes entirely.
 
-The framework is built on stable_baselines3 (torch) and OpenAI gym for the base environment class. But generally speaking, the model class is well isolated. Thus, the addition of competing libraries can be easily integrated into the existing framework. For the environment, it is inheriting from `gym.env` which means that it is necessary to write an entirely new environment in order to switch to a different library.
+The framework is built on stable_baselines3 (torch) and OpenAI gym for the base environment class. But generally speaking, the model class is well isolated. Thus, the addition of competing libraries can be easily integrated into the existing framework. For the environment, it is inheriting from `gym.Env` which means that it is necessary to write an entirely new environment in order to switch to a different library.
 
 ### Important considerations
 
@@ -173,7 +173,7 @@ class MyCoolRLModel(ReinforcementLearner):
     """
     class MyRLEnv(Base5ActionRLEnv):
         """
-        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        User made custom environment. This class inherits from BaseEnvironment and gym.Env.
         Users can override any functions from those parent classes. Here is an example
         of a user customized `calculate_reward()` function.
 
@@ -254,7 +254,7 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
 ```python
     class MyRLEnv(Base5ActionRLEnv):
         """
-        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        User made custom environment. This class inherits from BaseEnvironment and gym.Env.
         Users can override any functions from those parent classes. Here is an example
         of a user customized `calculate_reward()` function.
         """

docs/hyperopt.md

@@ -31,7 +31,7 @@ The docker-image includes hyperopt dependencies, no further action needed.
 ### Easy installation script (setup.sh) / Manual installation
 
 ```bash
-source .env/bin/activate
+source .venv/bin/activate
 pip install -r requirements-hyperopt.txt
 ```
 

docs/installation.md

@@ -143,11 +143,11 @@ If you are on Debian, Ubuntu or MacOS, freqtrade provides the script to install
 
 ### Activate your virtual environment
 
-Each time you open a new terminal, you must run `source .env/bin/activate` to activate your virtual environment.
+Each time you open a new terminal, you must run `source .venv/bin/activate` to activate your virtual environment.
 
 ```bash
-# then activate your .env
-source ./.env/bin/activate
+# activate virtual environment
+source ./.venv/bin/activate
 ```
 
 ### Congratulations
@@ -172,7 +172,7 @@ With this option, the script will install the bot and most dependencies:
 You will need to have git and python3.8+ installed beforehand for this to work.
 
 * Mandatory software as: `ta-lib`
-* Setup your virtualenv under `.env/`
+* Setup your virtualenv under `.venv/`
 
 This option is a combination of installation tasks and `--reset`
 
@@ -225,11 +225,11 @@ rm -rf ./ta-lib*
 You will run freqtrade in separated `virtual environment`
 
 ```bash
-# create virtualenv in directory /freqtrade/.env
-python3 -m venv .env
+# create virtualenv in directory /freqtrade/.venv
+python3 -m venv .venv
 
 # run virtualenv
-source .env/bin/activate
+source .venv/bin/activate
 ```
 
 #### Install python dependencies
@@ -383,7 +383,7 @@ You've made it this far, so you have successfully installed freqtrade.
 freqtrade create-userdir --userdir user_data
 
 # Step 2 - Create a new configuration file
-freqtrade new-config --config config.json
+freqtrade new-config --config user_data/config.json
 ```
 
 You are ready to run, read [Bot Configuration](configuration.md), remember to start with `dry_run: True` and verify that everything is working.
@@ -393,7 +393,7 @@ To learn how to setup your configuration, please refer to the [Bot Configuration
 ### Start the Bot
 
 ```bash
-freqtrade trade --config config.json --strategy SampleStrategy
+freqtrade trade --config user_data/config.json --strategy SampleStrategy
 ```
 
 !!! Warning
@@ -411,8 +411,8 @@ If you used (1)`Script` or (2)`Manual` installation, you need to run the bot in
 # if:
 bash: freqtrade: command not found
 
-# then activate your .env
-source ./.env/bin/activate
+# then activate your virtual environment
+source ./.venv/bin/activate
 ```
 
 ### MacOS installation error

docs/leverage.md

@@ -64,7 +64,7 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
 
 ##### Pair namings
 
-Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
+Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/#/README?id=perpetual-swap-perpetual-future).
 A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
 
 ### Margin mode

docs/lookahead-analysis.md

@@ -21,7 +21,10 @@ It also supports the lookahead-analysis of freqai strategies.
 
 - `--cache` is forced to "none".
 - `--max-open-trades` is forced to be at least equal to the number of pairs.
-- `--dry-run-wallet` is forced to be basically infinite.
+- `--dry-run-wallet` is forced to be basically infinite (1 billion).
+- `--stake-amount` is forced to be a static 10000 (10k).
+
+Those are set to avoid users accidentally generating false positives.
 
 ## Lookahead-analysis command reference
 

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.4.4
 mkdocs==1.5.2
-mkdocs-material==9.1.21
+mkdocs-material==9.2.1
 mdx_truly_sane_lists==1.3
 pymdown-extensions==10.1
 jinja2==3.1.2

docs/sandbox-testing.md

@@ -1,121 +0,0 @@
-# Sandbox API testing
-
-Some exchanges provide sandboxes or testbeds for risk-free testing, while running the bot against a real exchange.
-With some configuration, freqtrade (in combination with ccxt) provides access to these.
-
-This document is an overview to configure Freqtrade to be used with sandboxes.
-This can be useful to developers and trader alike.
-
-!!! Warning
-    Sandboxes usually have very low volume, and either a very wide spread, or no orders available at all.
-    Therefore, sandboxes will usually not do a good job of showing you how a strategy would work in real trading.
-
-## Exchanges known to have a sandbox / testnet
-
-* [binance](https://testnet.binance.vision/)
-* [coinbasepro](https://public.sandbox.pro.coinbase.com)
-* [gemini](https://exchange.sandbox.gemini.com/)
-* [huobipro](https://www.testnet.huobi.pro/)
-* [kucoin](https://sandbox.kucoin.com/)
-* [phemex](https://testnet.phemex.com/) 
-
-!!! Note
-    We did not test correct functioning of all of the above testnets. Please report your experiences with each sandbox.
-
----
-
-## Configure a Sandbox account
-
-When testing your API connectivity, make sure to use the appropriate sandbox / testnet URL.
-
-In general, you should follow these steps to enable an exchange's sandbox:
-
-* Figure out if an exchange has a sandbox (most likely by using google or the exchange's support documents)
-* Create a sandbox account (often the sandbox-account requires separate registration)
-* [Add some test assets to account](#add-test-funds)
-* Create API keys
-
-### Add test funds
-
-Usually, sandbox exchanges allow depositing funds directly via web-interface.
-You should make sure to have a realistic amount of funds available to your test-account, so results are representable of your real account funds.
-
-!!! Warning
-    Test exchanges will **NEVER** require your real credit card or banking details!
-
-## Configure freqtrade to use a exchange's sandbox
-
-### Sandbox URLs
-
-Freqtrade makes use of CCXT which in turn provides a list of URLs to Freqtrade.
-These include `['test']` and `['api']`.
-
-* `[Test]` if available will point to an Exchanges sandbox.
-* `[Api]` normally used, and resolves to live API target on the exchange.
-
-To make use of sandbox / test add "sandbox": true, to your config.json
-
-```json
-  "exchange": {
-        "name": "coinbasepro",
-        "sandbox": true,
-        "key": "5wowfxemogxeowo;heiohgmd",
-        "secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
-        "password": "1bkjfkhfhfu6sr",
-        "outdated_offset": 5
-        "pair_whitelist": [
-            "BTC/USD"
-        ]
-  },
-  "datadir": "user_data/data/coinbasepro_sandbox"
-```
-
-Also the following information:
-
-* api-key (created for the sandbox webpage)
-* api-secret (noted earlier)
-* password (the passphrase - noted earlier)
-
-!!! Tip "Different data directory"
-    We also recommend to set `datadir` to something identifying downloaded data as sandbox data, to avoid having sandbox data mixed with data from the real exchange.
-    This can be done by adding the `"datadir"` key to the configuration.
-    Now, whenever you use this configuration, your data directory will be set to this directory.
-
----
-
-## You should now be ready to test your sandbox
-
-Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox. Also make sure to select a pair which shows at least some decent value (which very often is BTC/<somestablecoin>).
-
-## Common problems with sandbox exchanges
-
-Sandbox exchange instances often have very low volume, which can cause some problems which usually are not seen on a real exchange instance.
-
-### Old Candles problem
-
-Since Sandboxes often have low volume, candles can be quite old and show no volume.
-To disable the error "Outdated history for pair ...", best increase the parameter `"outdated_offset"` to a number that seems realistic for the sandbox you're using.
-
-### Unfilled orders
-
-Sandboxes often have very low volumes - which means that many trades can go unfilled, or can go unfilled for a very long time.
-
-To mitigate this, you can try to match the first order on the opposite orderbook side using the following configuration:
-
-``` jsonc
-  "order_types": {
-    "entry": "limit",
-    "exit": "limit"
-    // ...
-  },
-  "entry_pricing": {
-    "price_side": "other",
-    // ...
-  },
-  "exit_pricing":{
-    "price_side": "other",
-    // ...
-  },
-  ```
-
-  The configuration is similar to the suggested configuration for market orders - however by using limit-orders you can avoid moving the price too much, and you can set the worst price you might get.

docs/windows_installation.md

@@ -31,8 +31,8 @@ Other versions must be downloaded from the above link.
 
 ``` powershell
 cd \path\freqtrade
-python -m venv .env
-.env\Scripts\activate.ps1
+python -m venv .venv
+.venv\Scripts\activate.ps1
 # optionally install ta-lib from wheel
 # Eventually adjust the below filename to match the downloaded wheel
 pip install --find-links build_helpers\ TA-Lib -U

freqtrade/commands/build_config_commands.py

@@ -10,7 +10,7 @@ from freqtrade.configuration.directory_operations import chown_user_directory
 from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, available_exchanges
-from freqtrade.misc import render_template
+from freqtrade.util import render_template
 
 
 logger = logging.getLogger(__name__)
@@ -105,7 +105,7 @@ def ask_user_config() -> Dict[str, Any]:
             "type": "select",
             "name": "exchange_name",
             "message": "Select exchange",
-            "choices": lambda x: [
+            "choices": [
                 "binance",
                 "binanceus",
                 "bittrex",

freqtrade/commands/cli_options.py

@@ -441,7 +441,7 @@ AVAILABLE_CLI_OPTIONS = {
     "dataformat_trades": Arg(
         '--data-format-trades',
         help='Storage format for downloaded trades data. (default: `feather`).',
-        choices=constants.AVAILABLE_DATAHANDLERS_TRADES,
+        choices=constants.AVAILABLE_DATAHANDLERS,
     ),
     "show_timerange": Arg(
         '--show-timerange',

freqtrade/commands/deploy_commands.py

@@ -10,7 +10,7 @@ from freqtrade.configuration.directory_operations import copy_sample_files, crea
 from freqtrade.constants import USERPATH_STRATEGIES
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import render_template, render_template_with_fallback
+from freqtrade.util import render_template, render_template_with_fallback
 
 
 logger = logging.getLogger(__name__)
@@ -35,6 +35,10 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
     Deploy new strategy from template to strategy_path
     """
     fallback = 'full'
+    attributes = render_template_with_fallback(
+        templatefile=f"strategy_subtemplates/strategy_attributes_{subtemplate}.j2",
+        templatefallbackfile=f"strategy_subtemplates/strategy_attributes_{fallback}.j2",
+    )
     indicators = render_template_with_fallback(
         templatefile=f"strategy_subtemplates/indicators_{subtemplate}.j2",
         templatefallbackfile=f"strategy_subtemplates/indicators_{fallback}.j2",
@@ -58,6 +62,7 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
 
     strategy_text = render_template(templatefile='base_strategy.py.j2',
                                     arguments={"strategy": strategy_name,
+                                               "attributes": attributes,
                                                "indicators": indicators,
                                                "buy_trend": buy_trend,
                                                "sell_trend": sell_trend,

freqtrade/commands/webserver_commands.py

@@ -7,9 +7,10 @@ def start_webserver(args: Dict[str, Any]) -> None:
     """
     Main entry point for webserver mode
     """
-    from freqtrade.configuration import Configuration
+    from freqtrade.configuration import setup_utils_configuration
     from freqtrade.rpc.api_server import ApiServer
 
     # Initialize configuration
-    config = Configuration(args, RunMode.WEBSERVER).get_config()
+
+    config = setup_utils_configuration(args, RunMode.WEBSERVER)
     ApiServer(config, standalone=True)

freqtrade/configuration/config_validation.py

@@ -51,6 +51,8 @@ def validate_config_schema(conf: Dict[str, Any], preliminary: bool = False) -> D
             conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
         else:
             conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED_FINAL
+    elif conf.get('runmode', RunMode.OTHER) == RunMode.WEBSERVER:
+        conf_schema['required'] = constants.SCHEMA_MINIMAL_WEBSERVER
     else:
         conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
     try:

freqtrade/configuration/environment_vars.py

@@ -41,7 +41,7 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
             key = env_var.replace(prefix, '')
             for k in reversed(key.split('__')):
                 val = {k.lower(): get_var_typed(val)
-                       if type(val) != dict and k not in no_convert else val}
+                       if not isinstance(val, dict) and k not in no_convert else val}
             relevant_vars = deep_merge_dicts(val, relevant_vars)
     return relevant_vars
 

freqtrade/constants.py

@@ -38,8 +38,7 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList', '
                        'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
 AVAILABLE_PROTECTIONS = ['CooldownPeriod',
                          'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
-AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5', 'feather']
-AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['parquet']
+AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5', 'feather', 'parquet']
 BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
 BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
 BACKTEST_CACHE_DEFAULT = 'day'
@@ -50,6 +49,15 @@ DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
 # Don't modify sequence of DEFAULT_TRADES_COLUMNS
 # it has wide consequences for stored trades files
 DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
+TRADES_DTYPES = {
+    'timestamp': 'int64',
+    'id': 'str',
+    'type': 'str',
+    'side': 'str',
+    'price': 'float64',
+    'amount': 'float64',
+    'cost': 'float64',
+}
 TRADING_MODES = ['spot', 'margin', 'futures']
 MARGIN_MODES = ['cross', 'isolated', '']
 
@@ -450,7 +458,7 @@ CONF_SCHEMA = {
         },
         'dataformat_trades': {
             'type': 'string',
-            'enum': AVAILABLE_DATAHANDLERS_TRADES,
+            'enum': AVAILABLE_DATAHANDLERS,
             'default': 'feather'
         },
         'position_adjustment_enable': {'type': 'boolean'},
@@ -461,7 +469,6 @@ CONF_SCHEMA = {
             'type': 'object',
             'properties': {
                 'name': {'type': 'string'},
-                'sandbox': {'type': 'boolean', 'default': False},
                 'key': {'type': 'string', 'default': ''},
                 'secret': {'type': 'string', 'default': ''},
                 'password': {'type': 'string', 'default': ''},
@@ -668,6 +675,9 @@ SCHEMA_MINIMAL_REQUIRED = [
     'dataformat_ohlcv',
     'dataformat_trades',
 ]
+SCHEMA_MINIMAL_WEBSERVER = SCHEMA_MINIMAL_REQUIRED + [
+    'api_server',
+]
 
 CANCEL_REASON = {
     "TIMEOUT": "cancelled due to timeout",

freqtrade/data/converter.py

@@ -1,16 +1,15 @@
 """
 Functions to convert data from one format to another
 """
-import itertools
 import logging
-from operator import itemgetter
 from typing import Dict, List
 
 import numpy as np
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, Config, TradeList
+from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TRADES_DTYPES,
+                                 Config, TradeList)
 from freqtrade.enums import CandleType, TradingMode
 
 
@@ -195,15 +194,14 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     return frame
 
 
-def trades_remove_duplicates(trades: List[List]) -> List[List]:
+def trades_df_remove_duplicates(trades: pd.DataFrame) -> pd.DataFrame:
     """
-    Removes duplicates from the trades list.
-    Uses itertools.groupby to avoid converting to pandas.
-    Tests show it as being pretty efficient on lists of 4M Lists.
-    :param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
-    :return: same format as above, but with duplicates removed
+    Removes duplicates from the trades DataFrame.
+    Uses pandas.DataFrame.drop_duplicates to remove duplicates based on the 'timestamp' column.
+    :param trades: DataFrame with the columns constants.DEFAULT_TRADES_COLUMNS
+    :return: DataFrame with duplicates removed based on the 'timestamp' column
     """
-    return [i for i, _ in itertools.groupby(sorted(trades, key=itemgetter(0)))]
+    return trades.drop_duplicates(subset=['timestamp', 'id'])
 
 
 def trades_dict_to_list(trades: List[Dict]) -> TradeList:
@@ -215,7 +213,32 @@ def trades_dict_to_list(trades: List[Dict]) -> TradeList:
     return [[t[col] for col in DEFAULT_TRADES_COLUMNS] for t in trades]
 
 
-def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
+def trades_convert_types(trades: DataFrame) -> DataFrame:
+    """
+    Convert Trades dtypes and add 'date' column
+    """
+    trades = trades.astype(TRADES_DTYPES)
+    trades['date'] = to_datetime(trades['timestamp'], unit='ms', utc=True)
+    return trades
+
+
+def trades_list_to_df(trades: TradeList, convert: bool = True):
+    """
+    convert trades list to dataframe
+    :param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
+    """
+    if not trades:
+        df = DataFrame(columns=DEFAULT_TRADES_COLUMNS)
+    else:
+        df = DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
+
+    if convert:
+        df = trades_convert_types(df)
+
+    return df
+
+
+def trades_to_ohlcv(trades: DataFrame, timeframe: str) -> DataFrame:
     """
     Converts trades list to OHLCV list
     :param trades: List of trades, as returned by ccxt.fetch_trades.
@@ -225,12 +248,9 @@ def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
     """
     from freqtrade.exchange import timeframe_to_minutes
     timeframe_minutes = timeframe_to_minutes(timeframe)
-    if not trades:
+    if trades.empty:
         raise ValueError('Trade-list empty.')
-    df = pd.DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
-    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms',
-                                     utc=True,)
-    df = df.set_index('timestamp')
+    df = trades.set_index('date', drop=True)
 
     df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
     df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()

freqtrade/data/dataprovider.py

@@ -17,7 +17,7 @@ from freqtrade.constants import (FULL_DATAFRAME_THRESHOLD, Config, ListPairsWith
 from freqtrade.data.history import load_pair_history
 from freqtrade.enums import CandleType, RPCMessageType, RunMode
 from freqtrade.exceptions import ExchangeError, OperationalException
-from freqtrade.exchange import Exchange, timeframe_to_seconds
+from freqtrade.exchange import Exchange, timeframe_to_prev_date, timeframe_to_seconds
 from freqtrade.exchange.types import OrderBook
 from freqtrade.misc import append_candles_to_dataframe
 from freqtrade.rpc import RPCManager
@@ -46,6 +46,8 @@ class DataProvider:
         self.__rpc = rpc
         self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
         self.__slice_index: Optional[int] = None
+        self.__slice_date: Optional[datetime] = None
+
         self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {}
         self.__producer_pairs_df: Dict[str,
                                        Dict[PairWithTimeframe, Tuple[DataFrame, datetime]]] = {}
@@ -64,10 +66,19 @@ class DataProvider:
     def _set_dataframe_max_index(self, limit_index: int):
         """
         Limit analyzed dataframe to max specified index.
+        Only relevant in backtesting.
         :param limit_index: dataframe index.
         """
         self.__slice_index = limit_index
 
+    def _set_dataframe_max_date(self, limit_date: datetime):
+        """
+        Limit infomrative dataframe to max specified index.
+        Only relevant in backtesting.
+        :param limit_date: "current date"
+        """
+        self.__slice_date = limit_date
+
     def _set_cached_df(
         self,
         pair: str,
@@ -284,7 +295,7 @@ class DataProvider:
     def historic_ohlcv(
         self,
         pair: str,
-        timeframe: Optional[str] = None,
+        timeframe: str,
         candle_type: str = ''
     ) -> DataFrame:
         """
@@ -307,7 +318,7 @@ class DataProvider:
             timerange.subtract_start(tf_seconds * startup_candles)
             self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
                 pair=pair,
-                timeframe=timeframe or self._config['timeframe'],
+                timeframe=timeframe,
                 datadir=self._config['datadir'],
                 timerange=timerange,
                 data_format=self._config['dataformat_ohlcv'],
@@ -354,7 +365,13 @@ class DataProvider:
             data = self.ohlcv(pair=pair, timeframe=timeframe, candle_type=candle_type)
         else:
             # Get historical OHLCV data (cached on disk).
+            timeframe = timeframe or self._config['timeframe']
             data = self.historic_ohlcv(pair=pair, timeframe=timeframe, candle_type=candle_type)
+            # Cut date to timeframe-specific date.
+            # This is necessary to prevent lookahead bias in callbacks through informative pairs.
+            if self.__slice_date:
+                cutoff_date = timeframe_to_prev_date(timeframe, self.__slice_date)
+                data = data.loc[data['date'] < cutoff_date]
         if len(data) == 0:
             logger.warning(f"No data found for ({pair}, {timeframe}, {candle_type}).")
         return data

freqtrade/data/history/featherdatahandler.py

@@ -4,7 +4,7 @@ from typing import Optional
 from pandas import DataFrame, read_feather, to_datetime
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
 from freqtrade.enums import CandleType
 
 from .idatahandler import IDataHandler
@@ -82,43 +82,41 @@ class FeatherDataHandler(IDataHandler):
         """
         raise NotImplementedError()
 
-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: DataFrame) -> None:
         """
         Store trades data (list of Dicts) to file
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
         filename = self._pair_trades_filename(self._datadir, pair)
         self.create_dir_if_needed(filename)
+        data.reset_index(drop=True).to_feather(filename, compression_level=9, compression='lz4')
 
-        tradesdata = DataFrame(data, columns=DEFAULT_TRADES_COLUMNS)
-        tradesdata.to_feather(filename, compression_level=9, compression='lz4')
-
-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: DataFrame):
         """
         Append data to existing files
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
         raise NotImplementedError()
 
-    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> DataFrame:
         """
         Load a pair from file, either .json.gz or .json
         # TODO: respect timerange ...
         :param pair: Load trades for this pair
         :param timerange: Timerange to load trades for - currently not implemented
-        :return: List of trades
+        :return: Dataframe containing trades
         """
         filename = self._pair_trades_filename(self._datadir, pair)
         if not filename.exists():
-            return []
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
 
         tradesdata = read_feather(filename)
 
-        return tradesdata.values.tolist()
+        return tradesdata
 
     @classmethod
     def _get_file_extension(cls):

freqtrade/data/history/hdf5datahandler.py

@@ -5,7 +5,7 @@ import numpy as np
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
 from freqtrade.enums import CandleType
 
 from .idatahandler import IDataHandler
@@ -100,42 +100,42 @@ class HDF5DataHandler(IDataHandler):
         """
         raise NotImplementedError()
 
-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: pd.DataFrame) -> None:
         """
         Store trades data (list of Dicts) to file
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
         key = self._pair_trades_key(pair)
 
-        pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS).to_hdf(
+        data.to_hdf(
             self._pair_trades_filename(self._datadir, pair), key,
             mode='a', complevel=9, complib='blosc',
             format='table', data_columns=['timestamp']
         )
 
-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: pd.DataFrame):
         """
         Append data to existing files
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
         raise NotImplementedError()
 
-    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> pd.DataFrame:
         """
         Load a pair from h5 file.
         :param pair: Load trades for this pair
         :param timerange: Timerange to load trades for - currently not implemented
-        :return: List of trades
+        :return: Dataframe containing trades
         """
         key = self._pair_trades_key(pair)
         filename = self._pair_trades_filename(self._datadir, pair)
 
         if not filename.exists():
-            return []
+            return pd.DataFrame(columns=DEFAULT_TRADES_COLUMNS)
         where = []
         if timerange:
             if timerange.starttype == 'date':
@@ -145,7 +145,7 @@ class HDF5DataHandler(IDataHandler):
 
         trades: pd.DataFrame = pd.read_hdf(filename, key=key, mode="r", where=where)
         trades[['id', 'type']] = trades[['id', 'type']].replace({np.nan: None})
-        return trades.values.tolist()
+        return trades
 
     @classmethod
     def _get_file_extension(cls):

freqtrade/data/history/history_utils.py

@@ -10,14 +10,16 @@ from freqtrade.configuration import TimeRange
 from freqtrade.constants import (DATETIME_PRINT_FORMAT, DEFAULT_DATAFRAME_COLUMNS,
                                  DL_DATA_TIMEFRAMES, Config)
 from freqtrade.data.converter import (clean_ohlcv_dataframe, ohlcv_to_dataframe,
-                                      trades_remove_duplicates, trades_to_ohlcv)
+                                      trades_df_remove_duplicates, trades_list_to_df,
+                                      trades_to_ohlcv)
 from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
 from freqtrade.enums import CandleType
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange
 from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
-from freqtrade.util import format_ms_time
+from freqtrade.util import dt_ts, format_ms_time
 from freqtrade.util.binance_mig import migrate_binance_futures_data
+from freqtrade.util.datetime_helpers import dt_now
 
 
 logger = logging.getLogger(__name__)
@@ -349,24 +351,27 @@ def _download_trades_history(exchange: Exchange,
         # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
         # DEFAULT_TRADES_COLUMNS: 1 -> id
 
-        if trades and since < trades[0][0]:
+        if not trades.empty and since > 0 and since < trades.iloc[0]['timestamp']:
             # since is before the first trade
-            logger.info(f"Start earlier than available data. Redownloading trades for {pair}...")
-            trades = []
+            logger.info(f"Start ({trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}) earlier than "
+                        f"available data. Redownloading trades for {pair}...")
+            trades = trades_list_to_df([])
 
-        if not since:
-            since = int((datetime.now() - timedelta(days=new_pairs_days)).timestamp()) * 1000
-
-        from_id = trades[-1][1] if trades else None
-        if trades and since < trades[-1][0]:
+        from_id = trades.iloc[-1]['id'] if not trades.empty else None
+        if not trades.empty and since < trades.iloc[-1]['timestamp']:
             # Reset since to the last available point
             # - 5 seconds (to ensure we're getting all trades)
-            since = trades[-1][0] - (5 * 1000)
+            since = trades.iloc[-1]['timestamp'] - (5 * 1000)
             logger.info(f"Using last trade date -5s - Downloading trades for {pair} "
                         f"since: {format_ms_time(since)}.")
 
-        logger.debug(f"Current Start: {format_ms_time(trades[0][0]) if trades else 'None'}")
-        logger.debug(f"Current End: {format_ms_time(trades[-1][0]) if trades else 'None'}")
+        if not since:
+            since = dt_ts(dt_now() - timedelta(days=new_pairs_days))
+
+        logger.debug("Current Start: %s", 'None' if trades.empty else
+                     f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}")
+        logger.debug("Current End: %s", 'None' if trades.empty else
+                     f"{trades.iloc[-1]['date']:{DATETIME_PRINT_FORMAT}}")
         logger.info(f"Current Amount of trades: {len(trades)}")
 
         # Default since_ms to 30 days if nothing is given
@@ -375,13 +380,16 @@ def _download_trades_history(exchange: Exchange,
                                                   until=until,
                                                   from_id=from_id,
                                                   )
-        trades.extend(new_trades[1])
+        new_trades_df = trades_list_to_df(new_trades[1])
+        trades = concat([trades, new_trades_df], axis=0)
         # Remove duplicates to make sure we're not storing data we don't need
-        trades = trades_remove_duplicates(trades)
+        trades = trades_df_remove_duplicates(trades)
         data_handler.trades_store(pair, data=trades)
 
-        logger.debug(f"New Start: {format_ms_time(trades[0][0])}")
-        logger.debug(f"New End: {format_ms_time(trades[-1][0])}")
+        logger.debug("New Start: %s", 'None' if trades.empty else
+                     f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}")
+        logger.debug("New End: %s", 'None' if trades.empty else
+                     f"{trades.iloc[-1]['date']:{DATETIME_PRINT_FORMAT}}")
         logger.info(f"New Amount of trades: {len(trades)}")
         return True
 

freqtrade/data/history/idatahandler.py

@@ -15,8 +15,9 @@ from pandas import DataFrame
 
 from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import ListPairsWithTimeframes, TradeList
-from freqtrade.data.converter import clean_ohlcv_dataframe, trades_remove_duplicates, trim_dataframe
+from freqtrade.constants import DEFAULT_TRADES_COLUMNS, ListPairsWithTimeframes
+from freqtrade.data.converter import (clean_ohlcv_dataframe, trades_convert_types,
+                                      trades_df_remove_duplicates, trim_dataframe)
 from freqtrade.enums import CandleType, TradingMode
 from freqtrade.exchange import timeframe_to_seconds
 
@@ -170,32 +171,42 @@ class IDataHandler(ABC):
         return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
 
     @abstractmethod
-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: DataFrame) -> None:
         """
         Store trades data (list of Dicts) to file
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
 
     @abstractmethod
-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: DataFrame):
         """
         Append data to existing files
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
 
     @abstractmethod
-    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> DataFrame:
         """
         Load a pair from file, either .json.gz or .json
         :param pair: Load trades for this pair
         :param timerange: Timerange to load trades for - currently not implemented
-        :return: List of trades
+        :return: Dataframe containing trades
         """
 
+    def trades_store(self, pair: str, data: DataFrame) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        # Filter on expected columns (will remove the actual date column).
+        self._trades_store(pair, data[DEFAULT_TRADES_COLUMNS])
+
     def trades_purge(self, pair: str) -> bool:
         """
         Remove data for this pair
@@ -208,7 +219,7 @@ class IDataHandler(ABC):
             return True
         return False
 
-    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> DataFrame:
         """
         Load a pair from file, either .json.gz or .json
         Removes duplicates in the process.
@@ -216,7 +227,10 @@ class IDataHandler(ABC):
         :param timerange: Timerange to load trades for - currently not implemented
         :return: List of trades
         """
-        return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
+        trades = trades_df_remove_duplicates(self._trades_load(pair, timerange=timerange))
+
+        trades = trades_convert_types(trades)
+        return trades
 
     @classmethod
     def create_dir_if_needed(cls, datadir: Path):

freqtrade/data/history/jsondatahandler.py

@@ -6,8 +6,8 @@ from pandas import DataFrame, read_json, to_datetime
 
 from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
-from freqtrade.data.converter import trades_dict_to_list
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
+from freqtrade.data.converter import trades_dict_to_list, trades_list_to_df
 from freqtrade.enums import CandleType
 
 from .idatahandler import IDataHandler
@@ -94,45 +94,46 @@ class JsonDataHandler(IDataHandler):
         """
         raise NotImplementedError()
 
-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: DataFrame) -> None:
         """
         Store trades data (list of Dicts) to file
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
         filename = self._pair_trades_filename(self._datadir, pair)
-        misc.file_dump_json(filename, data, is_zip=self._use_zip)
+        trades = data.values.tolist()
+        misc.file_dump_json(filename, trades, is_zip=self._use_zip)
 
-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: DataFrame):
         """
         Append data to existing files
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
         raise NotImplementedError()
 
-    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> DataFrame:
         """
         Load a pair from file, either .json.gz or .json
         # TODO: respect timerange ...
         :param pair: Load trades for this pair
         :param timerange: Timerange to load trades for - currently not implemented
-        :return: List of trades
+        :return: Dataframe containing trades
         """
         filename = self._pair_trades_filename(self._datadir, pair)
         tradesdata = misc.file_load_json(filename)
 
         if not tradesdata:
-            return []
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
 
         if isinstance(tradesdata[0], dict):
             # Convert trades dict to list
             logger.info("Old trades format detected - converting")
             tradesdata = trades_dict_to_list(tradesdata)
             pass
-        return tradesdata
+        return trades_list_to_df(tradesdata, convert=False)
 
     @classmethod
     def _get_file_extension(cls):

freqtrade/data/history/parquetdatahandler.py

@@ -4,7 +4,7 @@ from typing import Optional
 from pandas import DataFrame, read_parquet, to_datetime
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
 from freqtrade.enums import CandleType
 
 from .idatahandler import IDataHandler
@@ -81,25 +81,22 @@ class ParquetDataHandler(IDataHandler):
         """
         raise NotImplementedError()
 
-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: DataFrame) -> None:
         """
         Store trades data (list of Dicts) to file
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
-        # filename = self._pair_trades_filename(self._datadir, pair)
+        filename = self._pair_trades_filename(self._datadir, pair)
+        self.create_dir_if_needed(filename)
+        data.reset_index(drop=True).to_parquet(filename)
 
-        raise NotImplementedError()
-        # array = pa.array(data)
-        # array
-        # feather.write_feather(data, filename)
-
-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: DataFrame):
         """
         Append data to existing files
         :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                      column sequence as in DEFAULT_TRADES_COLUMNS
         """
         raise NotImplementedError()
@@ -112,14 +109,13 @@ class ParquetDataHandler(IDataHandler):
         :param timerange: Timerange to load trades for - currently not implemented
         :return: List of trades
         """
-        raise NotImplementedError()
-        # filename = self._pair_trades_filename(self._datadir, pair)
-        # tradesdata = misc.file_load_json(filename)
+        filename = self._pair_trades_filename(self._datadir, pair)
+        if not filename.exists():
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
 
-        # if not tradesdata:
-        #     return []
+        tradesdata = read_parquet(filename)
 
-        # return tradesdata
+        return tradesdata
 
     @classmethod
     def _get_file_extension(cls):

freqtrade/exchange/exchange.py

@@ -5,6 +5,7 @@ Cryptocurrency Exchanges support
 import asyncio
 import inspect
 import logging
+import signal
 from copy import deepcopy
 from datetime import datetime, timedelta, timezone
 from math import floor
@@ -263,8 +264,6 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(f"Initialization of ccxt failed. Reason: {e}") from e
 
-        self.set_sandbox(api, exchange_config, name)
-
         return api
 
     @property
@@ -465,16 +464,6 @@ class Exchange:
         return amount_to_contract_precision(amount, self.get_precision_amount(pair),
                                             self.precisionMode, contract_size)
 
-    def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None:
-        if exchange_config.get('sandbox'):
-            if api.urls.get('test'):
-                api.urls['api'] = api.urls['test']
-                logger.info("Enabled Sandbox API on %s", name)
-            else:
-                logger.warning(
-                    f"No Sandbox URL in CCXT for {name}, exiting. Please check your config.json")
-                raise OperationalException(f'Exchange {name} does not provide a sandbox api')
-
     def _load_async_markets(self, reload: bool = False) -> None:
         try:
             if self._api_async:
@@ -580,7 +569,7 @@ class Exchange:
         for pair in [f"{curr_1}/{curr_2}", f"{curr_2}/{curr_1}"]:
             if pair in self.markets and self.markets[pair].get('active'):
                 return pair
-        raise ExchangeError(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
+        raise ValueError(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
 
     def validate_timeframes(self, timeframe: Optional[str]) -> None:
         """
@@ -1876,7 +1865,7 @@ class Exchange:
                 tick = self.fetch_ticker(comb)
 
                 fee_to_quote_rate = safe_value_fallback2(tick, tick, 'last', 'ask')
-            except ExchangeError:
+            except (ValueError, ExchangeError):
                 fee_to_quote_rate = self._config['exchange'].get('unknown_fee_rate', None)
                 if not fee_to_quote_rate:
                     return None
@@ -2265,20 +2254,24 @@ class Exchange:
             from_id = t[-1][1]
             trades.extend(t[:-1])
         while True:
-            t = await self._async_fetch_trades(pair,
-                                               params={self._trades_pagination_arg: from_id})
-            if t:
-                # Skip last id since its the key for the next call
-                trades.extend(t[:-1])
-                if from_id == t[-1][1] or t[-1][0] > until:
-                    logger.debug(f"Stopping because from_id did not change. "
-                                 f"Reached {t[-1][0]} > {until}")
-                    # Reached the end of the defined-download period - add last trade as well.
-                    trades.extend(t[-1:])
-                    break
+            try:
+                t = await self._async_fetch_trades(pair,
+                                                   params={self._trades_pagination_arg: from_id})
+                if t:
+                    # Skip last id since its the key for the next call
+                    trades.extend(t[:-1])
+                    if from_id == t[-1][1] or t[-1][0] > until:
+                        logger.debug(f"Stopping because from_id did not change. "
+                                     f"Reached {t[-1][0]} > {until}")
+                        # Reached the end of the defined-download period - add last trade as well.
+                        trades.extend(t[-1:])
+                        break
 
-                from_id = t[-1][1]
-            else:
+                    from_id = t[-1][1]
+                else:
+                    break
+            except asyncio.CancelledError:
+                logger.debug("Async operation Interrupted, breaking trades DL loop.")
                 break
 
         return (pair, trades)
@@ -2298,16 +2291,20 @@ class Exchange:
         # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
         # DEFAULT_TRADES_COLUMNS: 1 -> id
         while True:
-            t = await self._async_fetch_trades(pair, since=since)
-            if t:
-                since = t[-1][0]
-                trades.extend(t)
-                # Reached the end of the defined-download period
-                if until and t[-1][0] > until:
-                    logger.debug(
-                        f"Stopping because until was reached. {t[-1][0]} > {until}")
+            try:
+                t = await self._async_fetch_trades(pair, since=since)
+                if t:
+                    since = t[-1][0]
+                    trades.extend(t)
+                    # Reached the end of the defined-download period
+                    if until and t[-1][0] > until:
+                        logger.debug(
+                            f"Stopping because until was reached. {t[-1][0]} > {until}")
+                        break
+                else:
                     break
-            else:
+            except asyncio.CancelledError:
+                logger.debug("Async operation Interrupted, breaking trades DL loop.")
                 break
 
         return (pair, trades)
@@ -2356,9 +2353,16 @@ class Exchange:
             raise OperationalException("This exchange does not support downloading Trades.")
 
         with self._loop_lock:
-            return self.loop.run_until_complete(
-                self._async_get_trade_history(pair=pair, since=since,
-                                              until=until, from_id=from_id))
+            task = asyncio.ensure_future(self._async_get_trade_history(
+                pair=pair, since=since, until=until, from_id=from_id))
+
+            for sig in [signal.SIGINT, signal.SIGTERM]:
+                try:
+                    self.loop.add_signal_handler(sig, task.cancel)
+                except NotImplementedError:
+                    # Not all platforms implement signals (e.g. windows)
+                    pass
+            return self.loop.run_until_complete(task)
 
     @retrier
     def _get_funding_fees_from_exchange(self, pair: str, since: Union[datetime, int]) -> float:

freqtrade/freqai/data_drawer.py

@@ -375,7 +375,7 @@ class FreqaiDataDrawer:
         num_keep = self.freqai_info["purge_old_models"]
         if not num_keep:
             return
-        elif type(num_keep) == bool:
+        elif isinstance(num_keep, bool):
             num_keep = 2
 
         model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -26,9 +26,9 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
             "model_training_parameters" : {
                 "learning_rate": 3e-4,
                 "trainer_kwargs": {
-                    "max_iters": 5000,
+                    "n_steps": 5000,
                     "batch_size": 64,
-                    "max_n_eval_batches": null,
+                    "n_epochs": null,
                 },
                 "model_kwargs": {
                     "hidden_dim": 512,

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -27,9 +27,9 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
             "model_training_parameters" : {
                 "learning_rate": 3e-4,
                 "trainer_kwargs": {
-                    "max_iters": 5000,
+                    "n_steps": 5000,
                     "batch_size": 64,
-                    "max_n_eval_batches": null,
+                    "n_epochs": null,
                 },
                 "model_kwargs": {
                     "hidden_dim": 512,

freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

@@ -30,9 +30,9 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
             "model_training_parameters" : {
                 "learning_rate": 3e-4,
                 "trainer_kwargs": {
-                    "max_iters": 5000,
+                    "n_steps": 5000,
                     "batch_size": 64,
-                    "max_n_eval_batches": null
+                    "n_epochs": null
                 },
                 "model_kwargs": {
                     "hidden_dim": 512,

freqtrade/freqai/torch/PyTorchDataConvertor.py

@@ -1,5 +1,4 @@
 from abc import ABC, abstractmethod
-from typing import Optional
 
 import pandas as pd
 import torch
@@ -12,14 +11,14 @@ class PyTorchDataConvertor(ABC):
     """
 
     @abstractmethod
-    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
+    def convert_x(self, df: pd.DataFrame, device: str) -> torch.Tensor:
         """
         :param df: "*_features" dataframe.
         :param device: The device to use for training (e.g. 'cpu', 'cuda').
         """
 
     @abstractmethod
-    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
+    def convert_y(self, df: pd.DataFrame, device: str) -> torch.Tensor:
         """
         :param df: "*_labels" dataframe.
         :param device: The device to use for training (e.g. 'cpu', 'cuda').
@@ -33,8 +32,8 @@ class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
 
     def __init__(
             self,
-            target_tensor_type: Optional[torch.dtype] = None,
-            squeeze_target_tensor: bool = False
+            target_tensor_type: torch.dtype = torch.float32,
+            squeeze_target_tensor: bool = False,
     ):
         """
         :param target_tensor_type: type of target tensor, for classification use
@@ -45,23 +44,14 @@ class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
         self._target_tensor_type = target_tensor_type
         self._squeeze_target_tensor = squeeze_target_tensor
 
-    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
-        x = torch.from_numpy(df.values).float()
-        if device:
-            x = x.to(device)
-
+    def convert_x(self, df: pd.DataFrame, device: str) -> torch.Tensor:
+        numpy_arrays = df.values
+        x = torch.tensor(numpy_arrays, device=device, dtype=torch.float32)
         return x
 
-    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
-        y = torch.from_numpy(df.values)
-
-        if self._target_tensor_type:
-            y = y.to(self._target_tensor_type)
-
+    def convert_y(self, df: pd.DataFrame, device: str) -> torch.Tensor:
+        numpy_arrays = df.values
+        y = torch.tensor(numpy_arrays, device=device, dtype=self._target_tensor_type)
         if self._squeeze_target_tensor:
             y = y.squeeze()
-
-        if device:
-            y = y.to(device)
-
         return y

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -1,5 +1,4 @@
 import logging
-import math
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
@@ -40,23 +39,27 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
             state_dict and model_meta_data saved by self.save() method.
         :param model_meta_data: Additional metadata about the model (optional).
         :param data_convertor: convertor from pd.DataFrame to torch.tensor.
-        :param max_iters: The number of training iterations to run.
-            iteration here refers to the number of times we call
-            self.optimizer.step(). used to calculate n_epochs.
+        :param n_steps: used to calculate n_epochs. The number of training iterations to run.
+            iteration here refers to the number of times optimizer.step() is called.
+            ignored if n_epochs is set.
+        :param n_epochs: The maximum number batches to use for evaluation.
         :param batch_size: The size of the batches to use during training.
-        :param max_n_eval_batches: The maximum number batches to use for evaluation.
         """
         self.model = model
         self.optimizer = optimizer
         self.criterion = criterion
         self.model_meta_data = model_meta_data
         self.device = device
-        self.max_iters: int = kwargs.get("max_iters", 100)
+        self.n_epochs: Optional[int] = kwargs.get("n_epochs", 10)
+        self.n_steps: Optional[int] = kwargs.get("n_steps", None)
+        if self.n_steps is None and not self.n_epochs:
+            raise Exception("Either `n_steps` or `n_epochs` should be set.")
+
         self.batch_size: int = kwargs.get("batch_size", 64)
-        self.max_n_eval_batches: Optional[int] = kwargs.get("max_n_eval_batches", None)
         self.data_convertor = data_convertor
         self.window_size: int = window_size
         self.tb_logger = tb_logger
+        self.test_batch_counter = 0
 
     def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]):
         """
@@ -72,55 +75,46 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
            backpropagation.
          - Updates the model's parameters using an optimizer.
         """
-        data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
-        epochs = self.calc_n_epochs(
-            n_obs=len(data_dictionary["train_features"]),
-            batch_size=self.batch_size,
-            n_iters=self.max_iters
-        )
         self.model.train()
-        for epoch in range(1, epochs + 1):
-            for i, batch_data in enumerate(data_loaders_dictionary["train"]):
 
+        data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
+        n_obs = len(data_dictionary["train_features"])
+        n_epochs = self.n_epochs or self.calc_n_epochs(n_obs=n_obs)
+        batch_counter = 0
+        for _ in range(n_epochs):
+            for _, batch_data in enumerate(data_loaders_dictionary["train"]):
                 xb, yb = batch_data
-                xb.to(self.device)
-                yb.to(self.device)
+                xb = xb.to(self.device)
+                yb = yb.to(self.device)
                 yb_pred = self.model(xb)
                 loss = self.criterion(yb_pred, yb)
 
                 self.optimizer.zero_grad(set_to_none=True)
                 loss.backward()
                 self.optimizer.step()
-                self.tb_logger.log_scalar("train_loss", loss.item(), i)
+                self.tb_logger.log_scalar("train_loss", loss.item(), batch_counter)
+                batch_counter += 1
 
             # evaluation
             if "test" in splits:
-                self.estimate_loss(
-                    data_loaders_dictionary,
-                    self.max_n_eval_batches,
-                    "test"
-                )
+                self.estimate_loss(data_loaders_dictionary, "test")
 
     @torch.no_grad()
     def estimate_loss(
             self,
             data_loader_dictionary: Dict[str, DataLoader],
-            max_n_eval_batches: Optional[int],
             split: str,
     ) -> None:
         self.model.eval()
-        n_batches = 0
-        for i, batch_data in enumerate(data_loader_dictionary[split]):
-            if max_n_eval_batches and i > max_n_eval_batches:
-                n_batches += 1
-                break
+        for _, batch_data in enumerate(data_loader_dictionary[split]):
             xb, yb = batch_data
-            xb.to(self.device)
-            yb.to(self.device)
+            xb = xb.to(self.device)
+            yb = yb.to(self.device)
 
             yb_pred = self.model(xb)
             loss = self.criterion(yb_pred, yb)
-            self.tb_logger.log_scalar(f"{split}_loss", loss.item(), i)
+            self.tb_logger.log_scalar(f"{split}_loss", loss.item(), self.test_batch_counter)
+            self.test_batch_counter += 1
 
         self.model.train()
 
@@ -148,31 +142,30 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
 
         return data_loader_dictionary
 
-    @staticmethod
-    def calc_n_epochs(n_obs: int, batch_size: int, n_iters: int) -> int:
+    def calc_n_epochs(self, n_obs: int) -> int:
         """
         Calculates the number of epochs required to reach the maximum number
         of iterations specified in the model training parameters.
 
-        the motivation here is that `max_iters` is easier to optimize and keep stable,
+        the motivation here is that `n_steps` is easier to optimize and keep stable,
         across different n_obs - the number of data points.
         """
+        assert isinstance(self.n_steps, int), "Either `n_steps` or `n_epochs` should be set."
+        n_batches = n_obs // self.batch_size
+        n_epochs = min(self.n_steps // n_batches, 1)
+        if n_epochs <= 10:
+            logger.warning(
+                f"Setting low n_epochs: {n_epochs}. "
+                f"Please consider increasing `n_steps` hyper-parameter."
+            )
 
-        n_batches = math.ceil(n_obs // batch_size)
-        epochs = math.ceil(n_iters // n_batches)
-        if epochs <= 10:
-            logger.warning("User set `max_iters` in such a way that the trainer will only perform "
-                           f" {epochs} epochs. Please consider increasing this value accordingly")
-            if epochs <= 1:
-                logger.warning("Epochs set to 1. Please review your `max_iters` value")
-                epochs = 1
-        return epochs
+        return n_epochs
 
     def save(self, path: Path):
         """
         - Saving any nn.Module state_dict
         - Saving model_meta_data, this dict should contain any additional data that the
-          user needs to store. e.g class_names for classification models.
+          user needs to store. e.g. class_names for classification models.
         """
 
         torch.save({

freqtrade/misc.py

@@ -192,30 +192,6 @@ def plural(num: float, singular: str, plural: Optional[str] = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
 
 
-def render_template(templatefile: str, arguments: dict = {}) -> str:
-
-    from jinja2 import Environment, PackageLoader, select_autoescape
-
-    env = Environment(
-        loader=PackageLoader('freqtrade', 'templates'),
-        autoescape=select_autoescape(['html', 'xml'])
-    )
-    template = env.get_template(templatefile)
-    return template.render(**arguments)
-
-
-def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
-                                  arguments: dict = {}) -> str:
-    """
-    Use templatefile if possible, otherwise fall back to templatefallbackfile
-    """
-    from jinja2.exceptions import TemplateNotFound
-    try:
-        return render_template(templatefile, arguments)
-    except TemplateNotFound:
-        return render_template(templatefallbackfile, arguments)
-
-
 def chunks(lst: List[Any], n: int) -> Iterator[List[Any]]:
     """
     Split lst into chunks of the size n.

freqtrade/optimize/backtesting.py

@@ -369,13 +369,14 @@ class Backtesting:
                 # Cleanup from prior runs
                 pair_data.drop(HEADERS[5:] + ['buy', 'sell'], axis=1, errors='ignore')
             df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})
-            # Trim startup period from analyzed dataframe
-            df_analyzed = processed[pair] = pair_data = trim_dataframe(
-                df_analyzed, self.timerange, startup_candles=self.required_startup)
             # Update dataprovider cache
             self.dataprovider._set_cached_df(
                 pair, self.timeframe, df_analyzed, self.config['candle_type_def'])
 
+            # Trim startup period from analyzed dataframe
+            df_analyzed = processed[pair] = pair_data = trim_dataframe(
+                df_analyzed, self.timerange, startup_candles=self.required_startup)
+
             # Create a copy of the dataframe before shifting, that way the entry signal/tag
             # remains on the correct candle for callbacks.
             df_analyzed = df_analyzed.copy()
@@ -567,8 +568,7 @@ class Backtesting:
             pos_trade = self._get_exit_for_signal(trade, row, exit_, amount)
             if pos_trade is not None:
                 order = pos_trade.orders[-1]
-                if self._get_order_filled(order.ft_price, row):
-                    order.close_bt_order(current_date, trade)
+                if self._try_close_open_order(order, trade, current_date, row):
                     trade.recalc_trade_from_orders()
                 self.wallets.update()
                 return pos_trade
@@ -579,6 +579,19 @@ class Backtesting:
         """ Rate is within candle, therefore filled"""
         return row[LOW_IDX] <= rate <= row[HIGH_IDX]
 
+    def _try_close_open_order(
+            self, order: Optional[Order], trade: LocalTrade, current_date: datetime,
+            row: Tuple) -> bool:
+        """
+        Check if an order is open and if it should've filled.
+        :return:  True if the order filled.
+        """
+        if order and self._get_order_filled(order.ft_price, row):
+            order.close_bt_order(current_date, trade)
+            trade.open_order_id = None
+            return True
+        return False
+
     def _get_exit_for_signal(
             self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple,
             amount: Optional[float] = None) -> Optional[LocalTrade]:
@@ -903,9 +916,7 @@ class Backtesting:
             )
             order._trade_bt = trade
             trade.orders.append(order)
-            if pos_adjust and self._get_order_filled(order.ft_price, row):
-                order.close_bt_order(current_time, trade)
-            else:
+            if not self._try_close_open_order(order, trade, current_time, row):
                 trade.open_order_id = str(self.order_id_counter)
             trade.recalc_trade_from_orders()
 
@@ -1121,23 +1132,18 @@ class Backtesting:
         for trade in list(LocalTrade.bt_trades_open_pp[pair]):
             # 3. Process entry orders.
             order = trade.select_order(trade.entry_side, is_open=True)
-            if order and self._get_order_filled(order.ft_price, row):
-                order.close_bt_order(current_time, trade)
-                trade.open_order_id = None
+            if self._try_close_open_order(order, trade, current_time, row):
                 self.wallets.update()
 
-                # 4. Create exit orders (if any)
+            # 4. Create exit orders (if any)
             if not trade.open_order_id:
                 self._check_trade_exit(trade, row)  # Place exit order if necessary
 
-                # 5. Process exit orders.
+            # 5. Process exit orders.
             order = trade.select_order(trade.exit_side, is_open=True)
-            if order and self._get_order_filled(order.ft_price, row):
-                order.close_bt_order(current_time, trade)
-                trade.open_order_id = None
+            if order and self._try_close_open_order(order, trade, current_time, row):
                 sub_trade = order.safe_amount_after_fee != trade.amount
                 if sub_trade:
-                    order.close_bt_order(current_time, trade)
                     trade.recalc_trade_from_orders()
                 else:
                     trade.close_date = current_time
@@ -1191,7 +1197,8 @@ class Backtesting:
 
                 row_index += 1
                 indexes[pair] = row_index
-                self.dataprovider._set_dataframe_max_index(row_index)
+                self.dataprovider._set_dataframe_max_index(self.required_startup + row_index)
+                self.dataprovider._set_dataframe_max_date(current_time)
                 current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
                 trade_dir: Optional[LongShort] = self.check_for_trade_entry(row)
 
@@ -1224,12 +1231,14 @@ class Backtesting:
                     is_first = True
                     current_time_det = current_time
                     for det_row in detail_data[HEADERS].values.tolist():
+                        self.dataprovider._set_dataframe_max_date(current_time_det)
                         open_trade_count_start = self.backtest_loop(
                             det_row, pair, current_time_det, end_date,
                             open_trade_count_start, trade_dir, is_first)
                         current_time_det += timedelta(minutes=self.timeframe_detail_min)
                         is_first = False
                 else:
+                    self.dataprovider._set_dataframe_max_date(current_time)
                     open_trade_count_start = self.backtest_loop(
                         row, pair, current_time, end_date,
                         open_trade_count_start, trade_dir)

freqtrade/optimize/lookahead_analysis.py

@@ -48,6 +48,7 @@ class LookaheadAnalysis:
         self.entry_varHolders: List[VarHolder] = []
         self.exit_varHolders: List[VarHolder] = []
         self.exchange: Optional[Any] = None
+        self._fee = None
 
         # pull variables the scope of the lookahead_analysis-instance
         self.local_config = deepcopy(config)
@@ -145,8 +146,13 @@ class LookaheadAnalysis:
                                             str(self.dt_to_timestamp(varholder.to_dt)))
         prepare_data_config['exchange']['pair_whitelist'] = pairs_to_load
 
+        if self._fee is not None:
+            # Don't re-calculate fee per pair, as fee might differ per pair.
+            prepare_data_config['fee'] = self._fee
+
         backtesting = Backtesting(prepare_data_config, self.exchange)
         self.exchange = backtesting.exchange
+        self._fee = backtesting.fee
         backtesting._set_strategy(backtesting.strategylist[0])
 
         varholder.data, varholder.timerange = backtesting.load_bt_data()
@@ -198,7 +204,7 @@ class LookaheadAnalysis:
         self.prepare_data(exit_varHolder, [result_row['pair']])
 
     # now we analyze a full trade of full_varholder and look for analyze its bias
-    def analyze_row(self, idx, result_row):
+    def analyze_row(self, idx: int, result_row):
         # if force-sold, ignore this signal since here it will unconditionally exit.
         if result_row.close_date == self.dt_to_timestamp(self.full_varHolder.to_dt):
             return
@@ -209,12 +215,16 @@ class LookaheadAnalysis:
         # fill entry_varHolder and exit_varHolder
         self.fill_entry_and_exit_varHolders(result_row)
 
+        # this will trigger a logger-message
+        buy_or_sell_biased: bool = False
+
         # register if buy signal is broken
         if not self.report_signal(
                 self.entry_varHolders[idx].result,
                 "open_date",
                 self.entry_varHolders[idx].compared_dt):
             self.current_analysis.false_entry_signals += 1
+            buy_or_sell_biased = True
 
         # register if buy or sell signal is broken
         if not self.report_signal(
@@ -222,6 +232,13 @@ class LookaheadAnalysis:
                 "close_date",
                 self.exit_varHolders[idx].compared_dt):
             self.current_analysis.false_exit_signals += 1
+            buy_or_sell_biased = True
+
+        if buy_or_sell_biased:
+            logger.info(f"found lookahead-bias in trade "
+                        f"pair: {result_row['pair']}, "
+                        f"timerange:{result_row['open_date']} - {result_row['close_date']}, "
+                        f"idx: {idx}")
 
         # check if the indicators themselves contain biased data
         self.analyze_indicators(self.full_varHolder, self.entry_varHolders[idx], result_row['pair'])
@@ -251,9 +268,33 @@ class LookaheadAnalysis:
         # starting from the same datetime to avoid miss-reports of bias
         for idx, result_row in self.full_varHolder.result['results'].iterrows():
             if self.current_analysis.total_signals == self.targeted_trade_amount:
+                logger.info(f"Found targeted trade amount = {self.targeted_trade_amount} signals.")
                 break
+            if found_signals < self.minimum_trade_amount:
+                logger.info(f"only found {found_signals} "
+                            f"which is smaller than "
+                            f"minimum trade amount = {self.minimum_trade_amount}. "
+                            f"Exiting this lookahead-analysis")
+                return None
+            if "force_exit" in result_row['exit_reason']:
+                logger.info("found force-exit in pair: {result_row['pair']}, "
+                            f"timerange:{result_row['open_date']}-{result_row['close_date']}, "
+                            f"idx: {idx}, skipping this one to avoid a false-positive.")
+
+                # just to keep the IDs of both full, entry and exit varholders the same
+                # to achieve a better debugging experience
+                self.entry_varHolders.append(VarHolder())
+                self.exit_varHolders.append(VarHolder())
+                continue
+
             self.analyze_row(idx, result_row)
 
+        if len(self.entry_varHolders) < self.minimum_trade_amount:
+            logger.info(f"only found {found_signals} after skipping forced exits "
+                        f"which is smaller than "
+                        f"minimum trade amount = {self.minimum_trade_amount}. "
+                        f"Exiting this lookahead-analysis")
+
         # Restore verbosity, so it's not too quiet for the next strategy
         restore_verbosity_for_bias_tester()
         # check and report signals

freqtrade/optimize/lookahead_analysis_helpers.py

@@ -137,6 +137,19 @@ class LookaheadAnalysisSubFunctions:
                         'just to avoid false positives')
             config['dry_run_wallet'] = min_dry_run_wallet
 
+        if 'timerange' not in config:
+            # setting a timerange is enforced here
+            raise OperationalException(
+                "Please set a timerange. "
+                "Usually a few months are enough depending on your needs and strategy."
+            )
+        # fix stake_amount to 10k.
+        # in a combination with a wallet size of 1 billion it should always be able to trade
+        # no matter if they use custom_stake_amount as a small percentage of wallet size
+        # or fixate custom_stake_amount to a certain value.
+        logger.info('fixing stake_amount to 10k')
+        config['stake_amount'] = 10000
+
         # enforce cache to be 'none', shift it to 'none' if not already
         # (since the default value is 'day')
         if config.get('backtest_cache') is None:

freqtrade/persistence/trade_model.py

@@ -648,11 +648,9 @@ class LocalTrade:
         """
         Method used internally to set self.stop_loss.
         """
-        stop_loss_norm = price_to_precision(stop_loss, self.price_precision, self.precision_mode,
-                                            rounding_mode=ROUND_DOWN if self.is_short else ROUND_UP)
         if not self.stop_loss:
-            self.initial_stop_loss = stop_loss_norm
-        self.stop_loss = stop_loss_norm
+            self.initial_stop_loss = stop_loss
+        self.stop_loss = stop_loss
 
         self.stop_loss_pct = -1 * abs(percent)
 
@@ -676,26 +674,27 @@ class LocalTrade:
         else:
             new_loss = float(current_price * (1 - abs(stoploss / leverage)))
 
+        stop_loss_norm = price_to_precision(new_loss, self.price_precision, self.precision_mode,
+                                            rounding_mode=ROUND_DOWN if self.is_short else ROUND_UP)
         # no stop loss assigned yet
         if self.initial_stop_loss_pct is None or refresh:
-            self.__set_stop_loss(new_loss, stoploss)
+            self.__set_stop_loss(stop_loss_norm, stoploss)
             self.initial_stop_loss = price_to_precision(
-                new_loss, self.price_precision, self.precision_mode,
+                stop_loss_norm, self.price_precision, self.precision_mode,
                 rounding_mode=ROUND_DOWN if self.is_short else ROUND_UP)
             self.initial_stop_loss_pct = -1 * abs(stoploss)
 
         # evaluate if the stop loss needs to be updated
         else:
-
-            higher_stop = new_loss > self.stop_loss
-            lower_stop = new_loss < self.stop_loss
+            higher_stop = stop_loss_norm > self.stop_loss
+            lower_stop = stop_loss_norm < self.stop_loss
 
             # stop losses only walk up, never down!,
             #   ? But adding more to a leveraged trade would create a lower liquidation price,
             #   ? decreasing the minimum stoploss
             if (higher_stop and not self.is_short) or (lower_stop and self.is_short):
                 logger.debug(f"{self.pair} - Adjusting stoploss...")
-                self.__set_stop_loss(new_loss, stoploss)
+                self.__set_stop_loss(stop_loss_norm, stoploss)
             else:
                 logger.debug(f"{self.pair} - Keeping current stoploss...")
 
@@ -769,10 +768,8 @@ class LocalTrade:
         self.open_order_id = None
         self.recalc_trade_from_orders(is_closing=True)
         if show_msg:
-            logger.info(
-                'Marking %s as closed as the trade is fulfilled and found no open orders for it.',
-                self
-            )
+            logger.info(f"Marking {self} as closed as the trade is fulfilled "
+                        "and found no open orders for it.")
 
     def update_fee(self, fee_cost: float, fee_currency: Optional[str], fee_rate: Optional[float],
                    side: str) -> None:
@@ -1220,12 +1217,13 @@ class LocalTrade:
             return LocalTrade.bt_open_open_trade_count
 
     @staticmethod
-    def stoploss_reinitialization(desired_stoploss):
+    def stoploss_reinitialization(desired_stoploss: float):
         """
         Adjust initial Stoploss to desired stoploss for all open trades.
         """
+        trade: Trade
         for trade in Trade.get_open_trades():
-            logger.info("Found open trade: %s", trade)
+            logger.info(f"Found open trade: {trade}")
 
             # skip case if trailing-stop changed the stoploss already.
             if (trade.stop_loss == trade.initial_stop_loss
@@ -1234,7 +1232,7 @@ class LocalTrade:
 
                 logger.info(f"Stoploss for {trade} needs adjustment...")
                 # Force reset of stoploss
-                trade.stop_loss = None
+                trade.stop_loss = 0.0
                 trade.initial_stop_loss_pct = None
                 trade.adjust_stop_loss(trade.open_rate, desired_stoploss)
                 logger.info(f"New stoploss: {trade.stop_loss}.")

freqtrade/rpc/api_server/api_schemas.py

@@ -1,7 +1,7 @@
 from datetime import date, datetime
 from typing import Any, Dict, List, Optional, Union
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict, RootModel, SerializeAsAny
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT, IntOrInf
 from freqtrade.enums import MarginMode, OrderTypeValues, SignalDirection, TradingMode
@@ -9,9 +9,9 @@ from freqtrade.types import ValidExchangesType
 
 
 class ExchangeModePayloadMixin(BaseModel):
-    trading_mode: Optional[TradingMode]
-    margin_mode: Optional[MarginMode]
-    exchange: Optional[str]
+    trading_mode: Optional[TradingMode] = None
+    margin_mode: Optional[MarginMode] = None
+    exchange: Optional[str] = None
 
 
 class Ping(BaseModel):
@@ -43,11 +43,11 @@ class BackgroundTaskStatus(BaseModel):
     job_category: str
     status: str
     running: bool
-    progress: Optional[float]
+    progress: Optional[float] = None
 
 
 class BackgroundTaskResult(BaseModel):
-    error: Optional[str]
+    error: Optional[str] = None
     status: str
 
 
@@ -60,9 +60,9 @@ class Balance(BaseModel):
     free: float
     balance: float
     used: float
-    bot_owned: Optional[float]
+    bot_owned: Optional[float] = None
     est_stake: float
-    est_stake_bot: Optional[float]
+    est_stake_bot: Optional[float] = None
     stake: str
     # Starting with 2.x
     side: str
@@ -141,7 +141,7 @@ class Profit(BaseModel):
     expectancy_ratio: float
     max_drawdown: float
     max_drawdown_abs: float
-    trading_volume: Optional[float]
+    trading_volume: Optional[float] = None
     bot_start_timestamp: int
     bot_start_date: str
 
@@ -173,50 +173,50 @@ class Daily(BaseModel):
 
 
 class UnfilledTimeout(BaseModel):
-    entry: Optional[int]
-    exit: Optional[int]
-    unit: Optional[str]
-    exit_timeout_count: Optional[int]
+    entry: Optional[int] = None
+    exit: Optional[int] = None
+    unit: Optional[str] = None
+    exit_timeout_count: Optional[int] = None
 
 
 class OrderTypes(BaseModel):
     entry: OrderTypeValues
     exit: OrderTypeValues
-    emergency_exit: Optional[OrderTypeValues]
-    force_exit: Optional[OrderTypeValues]
-    force_entry: Optional[OrderTypeValues]
+    emergency_exit: Optional[OrderTypeValues] = None
+    force_exit: Optional[OrderTypeValues] = None
+    force_entry: Optional[OrderTypeValues] = None
     stoploss: OrderTypeValues
     stoploss_on_exchange: bool
-    stoploss_on_exchange_interval: Optional[int]
+    stoploss_on_exchange_interval: Optional[int] = None
 
 
 class ShowConfig(BaseModel):
     version: str
-    strategy_version: Optional[str]
+    strategy_version: Optional[str] = None
     api_version: float
     dry_run: bool
     trading_mode: str
     short_allowed: bool
     stake_currency: str
     stake_amount: str
-    available_capital: Optional[float]
+    available_capital: Optional[float] = None
     stake_currency_decimals: int
     max_open_trades: IntOrInf
     minimal_roi: Dict[str, Any]
-    stoploss: Optional[float]
+    stoploss: Optional[float] = None
     stoploss_on_exchange: bool
-    trailing_stop: Optional[bool]
-    trailing_stop_positive: Optional[float]
-    trailing_stop_positive_offset: Optional[float]
-    trailing_only_offset_is_reached: Optional[bool]
-    unfilledtimeout: Optional[UnfilledTimeout]  # Empty in webserver mode
-    order_types: Optional[OrderTypes]
-    use_custom_stoploss: Optional[bool]
-    timeframe: Optional[str]
+    trailing_stop: Optional[bool] = None
+    trailing_stop_positive: Optional[float] = None
+    trailing_stop_positive_offset: Optional[float] = None
+    trailing_only_offset_is_reached: Optional[bool] = None
+    unfilledtimeout: Optional[UnfilledTimeout] = None  # Empty in webserver mode
+    order_types: Optional[OrderTypes] = None
+    use_custom_stoploss: Optional[bool] = None
+    timeframe: Optional[str] = None
     timeframe_ms: int
     timeframe_min: int
     exchange: str
-    strategy: Optional[str]
+    strategy: Optional[str] = None
     force_entry_enable: bool
     exit_pricing: Dict[str, Any]
     entry_pricing: Dict[str, Any]
@@ -231,17 +231,17 @@ class OrderSchema(BaseModel):
     pair: str
     order_id: str
     status: str
-    remaining: Optional[float]
+    remaining: Optional[float] = None
     amount: float
     safe_price: float
     cost: float
-    filled: Optional[float]
+    filled: Optional[float] = None
     ft_order_side: str
     order_type: str
     is_open: bool
-    order_timestamp: Optional[int]
-    order_filled_timestamp: Optional[int]
-    ft_fee_base: Optional[float]
+    order_timestamp: Optional[int] = None
+    order_filled_timestamp: Optional[int] = None
+    ft_fee_base: Optional[float] = None
 
 
 class TradeSchema(BaseModel):
@@ -255,81 +255,81 @@ class TradeSchema(BaseModel):
     amount: float
     amount_requested: float
     stake_amount: float
-    max_stake_amount: Optional[float]
+    max_stake_amount: Optional[float] = None
     strategy: str
-    enter_tag: Optional[str]
+    enter_tag: Optional[str] = None
     timeframe: int
-    fee_open: Optional[float]
-    fee_open_cost: Optional[float]
-    fee_open_currency: Optional[str]
-    fee_close: Optional[float]
-    fee_close_cost: Optional[float]
-    fee_close_currency: Optional[str]
+    fee_open: Optional[float] = None
+    fee_open_cost: Optional[float] = None
+    fee_open_currency: Optional[str] = None
+    fee_close: Optional[float] = None
+    fee_close_cost: Optional[float] = None
+    fee_close_currency: Optional[str] = None
 
     open_date: str
     open_timestamp: int
     open_rate: float
-    open_rate_requested: Optional[float]
+    open_rate_requested: Optional[float] = None
     open_trade_value: float
 
-    close_date: Optional[str]
-    close_timestamp: Optional[int]
-    close_rate: Optional[float]
-    close_rate_requested: Optional[float]
+    close_date: Optional[str] = None
+    close_timestamp: Optional[int] = None
+    close_rate: Optional[float] = None
+    close_rate_requested: Optional[float] = None
 
-    close_profit: Optional[float]
-    close_profit_pct: Optional[float]
-    close_profit_abs: Optional[float]
+    close_profit: Optional[float] = None
+    close_profit_pct: Optional[float] = None
+    close_profit_abs: Optional[float] = None
 
-    profit_ratio: Optional[float]
-    profit_pct: Optional[float]
-    profit_abs: Optional[float]
-    profit_fiat: Optional[float]
+    profit_ratio: Optional[float] = None
+    profit_pct: Optional[float] = None
+    profit_abs: Optional[float] = None
+    profit_fiat: Optional[float] = None
 
     realized_profit: float
-    realized_profit_ratio: Optional[float]
+    realized_profit_ratio: Optional[float] = None
 
-    exit_reason: Optional[str]
-    exit_order_status: Optional[str]
+    exit_reason: Optional[str] = None
+    exit_order_status: Optional[str] = None
 
-    stop_loss_abs: Optional[float]
-    stop_loss_ratio: Optional[float]
-    stop_loss_pct: Optional[float]
-    stoploss_order_id: Optional[str]
-    stoploss_last_update: Optional[str]
-    stoploss_last_update_timestamp: Optional[int]
-    initial_stop_loss_abs: Optional[float]
-    initial_stop_loss_ratio: Optional[float]
-    initial_stop_loss_pct: Optional[float]
+    stop_loss_abs: Optional[float] = None
+    stop_loss_ratio: Optional[float] = None
+    stop_loss_pct: Optional[float] = None
+    stoploss_order_id: Optional[str] = None
+    stoploss_last_update: Optional[str] = None
+    stoploss_last_update_timestamp: Optional[int] = None
+    initial_stop_loss_abs: Optional[float] = None
+    initial_stop_loss_ratio: Optional[float] = None
+    initial_stop_loss_pct: Optional[float] = None
 
-    min_rate: Optional[float]
-    max_rate: Optional[float]
-    open_order_id: Optional[str]
+    min_rate: Optional[float] = None
+    max_rate: Optional[float] = None
+    open_order_id: Optional[str] = None
     orders: List[OrderSchema]
 
-    leverage: Optional[float]
-    interest_rate: Optional[float]
-    liquidation_price: Optional[float]
-    funding_fees: Optional[float]
-    trading_mode: Optional[TradingMode]
+    leverage: Optional[float] = None
+    interest_rate: Optional[float] = None
+    liquidation_price: Optional[float] = None
+    funding_fees: Optional[float] = None
+    trading_mode: Optional[TradingMode] = None
 
-    amount_precision: Optional[float]
-    price_precision: Optional[float]
-    precision_mode: Optional[int]
+    amount_precision: Optional[float] = None
+    price_precision: Optional[float] = None
+    precision_mode: Optional[int] = None
 
 
 class OpenTradeSchema(TradeSchema):
-    stoploss_current_dist: Optional[float]
-    stoploss_current_dist_pct: Optional[float]
-    stoploss_current_dist_ratio: Optional[float]
-    stoploss_entry_dist: Optional[float]
-    stoploss_entry_dist_ratio: Optional[float]
+    stoploss_current_dist: Optional[float] = None
+    stoploss_current_dist_pct: Optional[float] = None
+    stoploss_current_dist_ratio: Optional[float] = None
+    stoploss_entry_dist: Optional[float] = None
+    stoploss_entry_dist_ratio: Optional[float] = None
     current_rate: float
     total_profit_abs: float
-    total_profit_fiat: Optional[float]
-    total_profit_ratio: Optional[float]
+    total_profit_fiat: Optional[float] = None
+    total_profit_ratio: Optional[float] = None
 
-    open_order: Optional[str]
+    open_order: Optional[str] = None
 
 
 class TradeResponse(BaseModel):
@@ -339,8 +339,7 @@ class TradeResponse(BaseModel):
     total_trades: int
 
 
-class ForceEnterResponse(BaseModel):
-    __root__: Union[TradeSchema, StatusMsg]
+ForceEnterResponse = RootModel[Union[TradeSchema, StatusMsg]]
 
 
 class LockModel(BaseModel):
@@ -352,7 +351,7 @@ class LockModel(BaseModel):
     lock_timestamp: int
     pair: str
     side: str
-    reason: Optional[str]
+    reason: Optional[str] = None
 
 
 class Locks(BaseModel):
@@ -361,8 +360,8 @@ class Locks(BaseModel):
 
 
 class DeleteLockRequest(BaseModel):
-    pair: Optional[str]
-    lockid: Optional[int]
+    pair: Optional[str] = None
+    lockid: Optional[int] = None
 
 
 class Logs(BaseModel):
@@ -373,17 +372,17 @@ class Logs(BaseModel):
 class ForceEnterPayload(BaseModel):
     pair: str
     side: SignalDirection = SignalDirection.LONG
-    price: Optional[float]
-    ordertype: Optional[OrderTypeValues]
-    stakeamount: Optional[float]
-    entry_tag: Optional[str]
-    leverage: Optional[float]
+    price: Optional[float] = None
+    ordertype: Optional[OrderTypeValues] = None
+    stakeamount: Optional[float] = None
+    entry_tag: Optional[str] = None
+    leverage: Optional[float] = None
 
 
 class ForceExitPayload(BaseModel):
     tradeid: str
-    ordertype: Optional[OrderTypeValues]
-    amount: Optional[float]
+    ordertype: Optional[OrderTypeValues] = None
+    amount: Optional[float] = None
 
 
 class BlacklistPayload(BaseModel):
@@ -405,7 +404,7 @@ class WhitelistResponse(BaseModel):
 
 
 class WhitelistEvaluateResponse(BackgroundTaskResult):
-    result: Optional[WhitelistResponse]
+    result: Optional[WhitelistResponse] = None
 
 
 class DeleteTrade(BaseModel):
@@ -420,8 +419,7 @@ class PlotConfig_(BaseModel):
     subplots: Dict[str, Any]
 
 
-class PlotConfig(BaseModel):
-    __root__: Union[PlotConfig_, Dict]
+PlotConfig = RootModel[Union[PlotConfig_, Dict]]
 
 
 class StrategyListResponse(BaseModel):
@@ -470,7 +468,7 @@ class PairHistory(BaseModel):
     timeframe: str
     timeframe_ms: int
     columns: List[str]
-    data: List[Any]
+    data: SerializeAsAny[List[Any]]
     length: int
     buy_signals: int
     sell_signals: int
@@ -484,11 +482,11 @@ class PairHistory(BaseModel):
     data_start: str
     data_stop: str
     data_stop_ts: int
-
-    class Config:
-        json_encoders = {
-            datetime: lambda v: v.strftime(DATETIME_PRINT_FORMAT),
-        }
+    # TODO[pydantic]: The following keys were removed: `json_encoders`.
+    # Check https://docs.pydantic.dev/dev-v2/migration/#changes-to-config for more information.
+    model_config = ConfigDict(json_encoders={
+        datetime: lambda v: v.strftime(DATETIME_PRINT_FORMAT),
+    })
 
 
 class BacktestFreqAIInputs(BaseModel):
@@ -497,16 +495,16 @@ class BacktestFreqAIInputs(BaseModel):
 
 class BacktestRequest(BaseModel):
     strategy: str
-    timeframe: Optional[str]
-    timeframe_detail: Optional[str]
-    timerange: Optional[str]
-    max_open_trades: Optional[IntOrInf]
-    stake_amount: Optional[str]
+    timeframe: Optional[str] = None
+    timeframe_detail: Optional[str] = None
+    timerange: Optional[str] = None
+    max_open_trades: Optional[IntOrInf] = None
+    stake_amount: Optional[Union[str, float]] = None
     enable_protections: bool
-    dry_run_wallet: Optional[float]
-    backtest_cache: Optional[str]
-    freqaimodel: Optional[str]
-    freqai: Optional[BacktestFreqAIInputs]
+    dry_run_wallet: Optional[float] = None
+    backtest_cache: Optional[str] = None
+    freqaimodel: Optional[str] = None
+    freqai: Optional[BacktestFreqAIInputs] = None
 
 
 class BacktestResponse(BaseModel):
@@ -515,9 +513,9 @@ class BacktestResponse(BaseModel):
     status_msg: str
     step: str
     progress: float
-    trade_count: Optional[float]
+    trade_count: Optional[float] = None
     # TODO: Properly type backtestresult...
-    backtest_result: Optional[Dict[str, Any]]
+    backtest_result: Optional[Dict[str, Any]] = None
 
 
 # TODO: This is a copy of BacktestHistoryEntryType
@@ -540,5 +538,5 @@ class SysInfo(BaseModel):
 
 
 class Health(BaseModel):
-    last_process: Optional[datetime]
-    last_process_ts: Optional[int]
+    last_process: Optional[datetime] = None
+    last_process_ts: Optional[int] = None

freqtrade/rpc/api_server/api_v1.py

@@ -175,9 +175,9 @@ def force_entry(payload: ForceEnterPayload, rpc: RPC = Depends(get_rpc)):
                                  leverage=payload.leverage)
 
     if trade:
-        return ForceEnterResponse.parse_obj(trade.to_json())
+        return ForceEnterResponse.model_validate(trade.to_json())
     else:
-        return ForceEnterResponse.parse_obj(
+        return ForceEnterResponse.model_validate(
             {"status": f"Error entering {payload.side} trade for pair {payload.pair}."})
 
 
@@ -282,14 +282,14 @@ def plot_config(strategy: Optional[str] = None, config=Depends(get_config),
     if not strategy:
         if not rpc:
             raise RPCException("Strategy is mandatory in webserver mode.")
-        return PlotConfig.parse_obj(rpc._rpc_plot_config())
+        return PlotConfig.model_validate(rpc._rpc_plot_config())
     else:
         config1 = deepcopy(config)
         config1.update({
             'strategy': strategy
         })
     try:
-        return PlotConfig.parse_obj(RPC._rpc_plot_config_with_strategy(config1))
+        return PlotConfig.model_validate(RPC._rpc_plot_config_with_strategy(config1))
     except Exception as e:
         raise HTTPException(status_code=502, detail=str(e))
 

freqtrade/rpc/api_server/api_ws.py

@@ -65,7 +65,7 @@ async def _process_consumer_request(
     """
     # Validate the request, makes sure it matches the schema
     try:
-        websocket_request = WSRequestSchema.parse_obj(request)
+        websocket_request = WSRequestSchema.model_validate(request)
     except ValidationError as e:
         logger.error(f"Invalid request from {channel}: {e}")
         return
@@ -94,7 +94,7 @@ async def _process_consumer_request(
 
         # Format response
         response = WSWhitelistMessage(data=whitelist)
-        await channel.send(response.dict(exclude_none=True))
+        await channel.send(response.model_dump(exclude_none=True))
 
     elif type_ == RPCRequestType.ANALYZED_DF:
         # Limit the amount of candles per dataframe to 'limit' or 1500
@@ -105,7 +105,7 @@ async def _process_consumer_request(
         for message in rpc._ws_request_analyzed_df(limit, pair):
             # Format response
             response = WSAnalyzedDFMessage(data=message)
-            await channel.send(response.dict(exclude_none=True))
+            await channel.send(response.model_dump(exclude_none=True))
 
 
 @router.websocket("/message/ws")

freqtrade/rpc/api_server/ws_schemas.py

@@ -2,15 +2,14 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional, TypedDict
 
 from pandas import DataFrame
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 from freqtrade.constants import PairWithTimeframe
 from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType
 
 
 class BaseArbitraryModel(BaseModel):
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(arbitrary_types_allowed=True)
 
 
 class WSRequestSchema(BaseArbitraryModel):
@@ -27,9 +26,7 @@ class WSMessageSchemaType(TypedDict):
 class WSMessageSchema(BaseArbitraryModel):
     type: RPCMessageType
     data: Optional[Any] = None
-
-    class Config:
-        extra = 'allow'
+    model_config = ConfigDict(extra='allow')
 
 
 # ------------------------------ REQUEST SCHEMAS ----------------------------

freqtrade/rpc/external_message_consumer.py

@@ -41,7 +41,7 @@ logger = logging.getLogger(__name__)
 
 
 def schema_to_dict(schema: Union[WSMessageSchema, WSRequestSchema]):
-    return schema.dict(exclude_none=True)
+    return schema.model_dump(exclude_none=True)
 
 
 class ExternalMessageConsumer:
@@ -322,7 +322,7 @@ class ExternalMessageConsumer:
         producer_name = producer.get('name', 'default')
 
         try:
-            producer_message = WSMessageSchema.parse_obj(message)
+            producer_message = WSMessageSchema.model_validate(message)
         except ValidationError as e:
             logger.error(f"Invalid message from `{producer_name}`: {e}")
             return
@@ -344,7 +344,7 @@ class ExternalMessageConsumer:
     def _consume_whitelist_message(self, producer_name: str, message: WSMessageSchema):
         try:
             # Validate the message
-            whitelist_message = WSWhitelistMessage.parse_obj(message)
+            whitelist_message = WSWhitelistMessage.model_validate(message.model_dump())
         except ValidationError as e:
             logger.error(f"Invalid message from `{producer_name}`: {e}")
             return
@@ -356,7 +356,7 @@ class ExternalMessageConsumer:
 
     def _consume_analyzed_df_message(self, producer_name: str, message: WSMessageSchema):
         try:
-            df_message = WSAnalyzedDFMessage.parse_obj(message)
+            df_message = WSAnalyzedDFMessage.model_validate(message.model_dump())
         except ValidationError as e:
             logger.error(f"Invalid message from `{producer_name}`: {e}")
             return

freqtrade/rpc/fiat_convert.py

@@ -26,6 +26,7 @@ coingecko_mapping = {
     'sol': 'solana',
     'usdt': 'tether',
     'busd': 'binance-usd',
+    'tusd': 'true-usd',
 }
 
 

freqtrade/rpc/rpc.py

@@ -612,17 +612,13 @@ class RPC:
                 est_stake = balance.free
             est_bot_stake = amount
         else:
-            try:
-                pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency)
-                rate: Optional[float] = tickers.get(pair, {}).get('last', None)
-                if rate:
-                    if pair.startswith(stake_currency) and not pair.endswith(stake_currency):
-                        rate = 1.0 / rate
-                    est_stake = rate * balance.total
-                    est_bot_stake = rate * amount
-            except (ExchangeError):
-                logger.warning(f"Could not get rate for pair {coin}.")
-                raise ValueError()
+            pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency)
+            rate: Optional[float] = tickers.get(pair, {}).get('last', None)
+            if rate:
+                if pair.startswith(stake_currency) and not pair.endswith(stake_currency):
+                    rate = 1.0 / rate
+                est_stake = rate * balance.total
+                est_bot_stake = rate * amount
 
         return est_stake, est_bot_stake
 

freqtrade/strategy/interface.py

@@ -381,7 +381,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
         For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
 
-        When not implemented by a strategy, returns the initial stoploss value
+        When not implemented by a strategy, returns the initial stoploss value.
         Only called when use_custom_stoploss is set to True.
 
         :param pair: Pair that's currently analyzed
@@ -1181,7 +1181,8 @@ class IStrategy(ABC, HyperStrategyMixin):
         bound = (low if trade.is_short else high)
         bound_profit = current_profit if not bound else trade.calc_profit_ratio(bound)
         if self.use_custom_stoploss and dir_correct:
-            stop_loss_value = strategy_safe_wrapper(self.custom_stoploss, default_retval=None
+            stop_loss_value = strategy_safe_wrapper(self.custom_stoploss, default_retval=None,
+                                                    supress_error=True
                                                     )(pair=trade.pair, trade=trade,
                                                       current_time=current_time,
                                                       current_rate=(bound or current_rate),

freqtrade/templates/base_strategy.py.j2

@@ -78,19 +78,7 @@ class {{ strategy }}(IStrategy):
     buy_rsi = IntParameter(10, 40, default=30, space="buy")
     sell_rsi = IntParameter(60, 90, default=70, space="sell")
 
-    # Optional order type mapping.
-    order_types = {
-        'entry': 'limit',
-        'exit': 'limit',
-        'stoploss': 'market',
-        'stoploss_on_exchange': False
-    }
-
-    # Optional order time in force.
-    order_time_in_force = {
-        'entry': 'GTC',
-        'exit': 'GTC'
-    }
+    {{ attributes | indent(4) }}
     {{ plot_config | indent(4) }}
 
     def informative_pairs(self):

freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2

@@ -0,0 +1,13 @@
+# Optional order type mapping.
+order_types = {
+    'entry': 'limit',
+    'exit': 'limit',
+    'stoploss': 'market',
+    'stoploss_on_exchange': False
+}
+
+# Optional order time in force.
+order_time_in_force = {
+    'entry': 'GTC',
+    'exit': 'GTC'
+}

freqtrade/util/__init__.py

@@ -2,6 +2,7 @@ from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humani
                                              dt_utc, format_ms_time, shorten_date)
 from freqtrade.util.ft_precise import FtPrecise
 from freqtrade.util.periodic_cache import PeriodicCache
+from freqtrade.util.template_renderer import render_template, render_template_with_fallback  # noqa
 
 
 __all__ = [

freqtrade/util/template_renderer.py

@@ -0,0 +1,27 @@
+"""
+Jinja2 rendering utils, used to generate new strategy and configurations.
+"""
+
+
+def render_template(templatefile: str, arguments: dict = {}) -> str:
+
+    from jinja2 import Environment, PackageLoader, select_autoescape
+
+    env = Environment(
+        loader=PackageLoader('freqtrade', 'templates'),
+        autoescape=select_autoescape(['html', 'xml'])
+    )
+    template = env.get_template(templatefile)
+    return template.render(**arguments)
+
+
+def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
+                                  arguments: dict = {}) -> str:
+    """
+    Use templatefile if possible, otherwise fall back to templatefallbackfile
+    """
+    from jinja2.exceptions import TemplateNotFound
+    try:
+        return render_template(templatefile, arguments)
+    except TemplateNotFound:
+        return render_template(templatefallbackfile, arguments)

mkdocs.yml

@@ -47,7 +47,6 @@ nav:
         - Advanced Hyperopt: advanced-hyperopt.md
         - Producer/Consumer mode: producer-consumer.md
         - Edge Positioning: edge.md
-        - Sandbox Testing: sandbox-testing.md
     - FAQ: faq.md
     - SQL Cheat-sheet: sql_cheatsheet.md
     - Strategy migration: strategy_migration.md

pyproject.toml

@@ -63,7 +63,7 @@ ignore = ["freqtrade/vendor/**"]
 
 [tool.ruff]
 line-length = 100
-extend-exclude = [".env"]
+extend-exclude = [".env", ".venv"]
 target-version = "py38"
 extend-select = [
   "C90",    # mccabe

requirements-dev.txt

@@ -7,8 +7,8 @@
 -r docs/requirements-docs.txt
 
 coveralls==3.3.1
-ruff==0.0.282
-mypy==1.4.1
+ruff==0.0.285
+mypy==1.5.1
 pre-commit==3.3.3
 pytest==7.4.0
 pytest-asyncio==0.21.1
@@ -17,10 +17,10 @@ pytest-mock==3.11.1
 pytest-random-order==1.1.0
 isort==5.12.0
 # For datetime mocking
-time-machine==2.11.0
+time-machine==2.12.0
 
 # Convert jupyter notebooks to markdown documents
-nbconvert==7.7.3
+nbconvert==7.7.4
 
 # mypy types
 types-cachetools==5.3.0.6

requirements-freqai-rl.txt

@@ -4,8 +4,8 @@
 # Required for freqai-rl
 torch==2.0.1
 #until these branches will be released we can use this
-gymnasium==0.28.1
-stable_baselines3==2.0.0
+gymnasium==0.29.1
+stable_baselines3==2.1.0
 sb3_contrib>=2.0.0a9
 # Progress bar for stable-baselines3 and sb3-contrib
-tqdm==4.65.0
+tqdm==4.66.1

requirements-freqai.txt

@@ -4,9 +4,9 @@
 
 # Required for freqai
 scikit-learn==1.1.3
-joblib==1.3.1
+joblib==1.3.2
 catboost==1.2; 'arm' not in platform_machine
 lightgbm==4.0.0
 xgboost==1.7.6
-tensorboard==2.13.0
+tensorboard==2.14.0
 datasieve==0.1.7

requirements-hyperopt.txt

@@ -2,7 +2,7 @@
 -r requirements.txt
 
 # Required for hyperopt
-scipy==1.11.1; python_version >= '3.9'
+scipy==1.11.2; python_version >= '3.9'
 scipy==1.10.1; python_version < '3.9'
 scikit-learn==1.1.3
 scikit-optimize==0.9.0

requirements-plot.txt

@@ -1,4 +1,4 @@
 # Include all requirements to run the bot.
 -r requirements.txt
 
-plotly==5.15.0
+plotly==5.16.1

requirements.txt

@@ -3,11 +3,11 @@ numpy==1.24.3; python_version <= '3.8'
 pandas==2.0.3
 pandas-ta==0.3.14b
 
-ccxt==4.0.50
+ccxt==4.0.71
 cryptography==41.0.3; platform_machine != 'armv7l'
 cryptography==40.0.1; platform_machine == 'armv7l'
 aiohttp==3.8.5
-SQLAlchemy==2.0.19
+SQLAlchemy==2.0.20
 python-telegram-bot==20.4
 # can't be hard-pinned due to telegram-bot pinning httpx with ~
 httpx>=0.24.1
@@ -15,15 +15,15 @@ arrow==1.2.3
 cachetools==5.3.1
 requests==2.31.0
 urllib3==2.0.4
-jsonschema==4.18.6
-TA-Lib==0.4.27
+jsonschema==4.19.0
+TA-Lib==0.4.28
 technical==1.4.0
 tabulate==0.9.0
 pycoingecko==3.1.0
 jinja2==3.1.2
 tables==3.8.0
 blosc==1.11.1
-joblib==1.3.1
+joblib==1.3.2
 rich==13.5.2
 pyarrow==12.0.1; platform_machine != 'armv7l'
 
@@ -33,24 +33,24 @@ py_find_1st==1.1.5
 # Load ticker files 30% faster
 python-rapidjson==1.10
 # Properly format api responses
-orjson==3.9.3
+orjson==3.9.5
 
 # Notify systemd
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.101.0
-pydantic==1.10.11
+fastapi==0.101.1
+pydantic==2.2.1
 uvicorn==0.23.2
 pyjwt==2.8.0
-aiofiles==23.1.0
+aiofiles==23.2.1
 psutil==5.9.5
 
 # Support for colorized terminal output
 colorama==0.4.6
 # Building config files interactively
-questionary==1.10.0
-prompt-toolkit==3.0.39
+questionary==2.0.0
+prompt-toolkit==3.0.36
 # Extensions to datetime library
 python-dateutil==2.8.2
 

setup.py

@@ -97,7 +97,7 @@ setup(
         'rich',
         'pyarrow; platform_machine != "armv7l"',
         'fastapi',
-        'pydantic>=1.8.0,<2.0',
+        'pydantic>=2.2.0',
         'uvicorn',
         'psutil',
         'pyjwt',

setup.sh

@@ -11,7 +11,7 @@ function check_installed_pip() {
    ${PYTHON} -m pip > /dev/null
    if [ $? -ne 0 ]; then
         echo_block "Installing Pip for ${PYTHON}"
-        curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
+        curl https://bootstrap.pypa.io/get-pip.py -s -o get-pip.py
         ${PYTHON} get-pip.py
         rm get-pip.py
    fi
@@ -41,12 +41,12 @@ function check_installed_python() {
 }
 
 function updateenv() {
-    echo_block "Updating your virtual env"
-    if [ ! -f .env/bin/activate ]; then
+    echo_block "Updating your virtual environment"
+    if [ ! -f .venv/bin/activate ]; then
         echo "Something went wrong, no virtual environment found."
         exit 1
     fi
-    source .env/bin/activate
+    source .venv/bin/activate
     SYS_ARCH=$(uname -m)
     echo "pip install in-progress. Please wait..."
     ${PYTHON} -m pip install --upgrade pip wheel setuptools
@@ -120,7 +120,7 @@ function updateenv() {
 
 # Install tab lib
 function install_talib() {
-    if [ -f /usr/local/lib/libta_lib.a ]; then
+    if [ -f /usr/local/lib/libta_lib.a ] || [ -f /usr/local/lib/libta_lib.so ] || [ -f /usr/lib/libta_lib.so ]; then
         echo "ta-lib already installed, skipping"
         return
     fi
@@ -186,7 +186,14 @@ function install_redhat() {
 # Upgrade the bot
 function update() {
     git pull
+    if [ -f .env/bin/activate  ]; then
+        # Old environment found - updating to new environment.
+        recreate_environments
+    fi
     updateenv
+    echo "Update completed."
+    echo_block "Don't forget to activate your virtual enviorment with 'source .venv/bin/activate'!"
+
 }
 
 function check_git_changes() {
@@ -199,6 +206,27 @@ function check_git_changes() {
     fi
 }
 
+function recreate_environments() {
+    if [ -d ".env" ]; then
+        # Remove old virtual env
+        echo "- Deleting your previous virtual env"
+        echo "Warning: Your new environment will be at .venv!"
+        rm -rf .env
+    fi
+    if [ -d ".venv" ]; then
+        echo "- Deleting your previous virtual env"
+        rm -rf .venv
+    fi
+
+    echo
+    ${PYTHON} -m venv .venv
+    if [ $? -ne 0 ]; then
+        echo "Could not create virtual environment. Leaving now"
+        exit 1
+    fi
+
+}
+
 # Reset Develop or Stable branch
 function reset() {
     echo_block "Resetting branch and virtual env"
@@ -225,22 +253,13 @@ function reset() {
     else
         echo "Reset ignored because you are not on 'stable' or 'develop'."
     fi
+    recreate_environments
 
-    if [ -d ".env" ]; then
-        echo "- Deleting your previous virtual env"
-        rm -rf .env
-    fi
-    echo
-    ${PYTHON} -m venv .env
-    if [ $? -ne 0 ]; then
-        echo "Could not create virtual environment. Leaving now"
-        exit 1
-    fi
     updateenv
 }
 
 function config() {
-    echo_block "Please use 'freqtrade new-config -c config.json' to generate a new configuration file."
+    echo_block "Please use 'freqtrade new-config -c user_data/config.json' to generate a new configuration file."
 }
 
 function install() {
@@ -266,9 +285,9 @@ function install() {
     reset
     config
     echo_block "Run the bot !"
-    echo "You can now use the bot by executing 'source .env/bin/activate; freqtrade <subcommand>'."
-    echo "You can see the list of available bot sub-commands by executing 'source .env/bin/activate; freqtrade --help'."
-    echo "You verify that freqtrade is installed successfully by running 'source .env/bin/activate; freqtrade --version'."
+    echo "You can now use the bot by executing 'source .venv/bin/activate; freqtrade <subcommand>'."
+    echo "You can see the list of available bot sub-commands by executing 'source .venv/bin/activate; freqtrade --help'."
+    echo "You verify that freqtrade is installed successfully by running 'source .venv/bin/activate; freqtrade --version'."
 }
 
 function plot() {

tests/conftest.py

@@ -14,7 +14,7 @@ import pytest
 
 from freqtrade import constants
 from freqtrade.commands import Arguments
-from freqtrade.data.converter import ohlcv_to_dataframe
+from freqtrade.data.converter import ohlcv_to_dataframe, trades_list_to_df
 from freqtrade.edge import PairInfo
 from freqtrade.enums import CandleType, MarginMode, RunMode, SignalDirection, TradingMode
 from freqtrade.exchange import Exchange
@@ -2346,7 +2346,15 @@ def trades_history():
             [1565798399629, '1261813bb30', None, 'buy', 0.019627, 0.244, 0.004788987999999999],
             [1565798399752, '1261813cc31', None, 'sell', 0.019626, 0.011, 0.00021588599999999999],
             [1565798399862, '126181cc332', None, 'sell', 0.019626, 0.011, 0.00021588599999999999],
-            [1565798399872, '1261aa81333', None, 'sell', 0.019626, 0.011, 0.00021588599999999999]]
+            [1565798399862, '126181cc333', None, 'sell', 0.019626, 0.012, 0.00021588599999999999],
+            [1565798399872, '1261aa81334', None, 'sell', 0.019626, 0.011, 0.00021588599999999999]]
+
+
+@pytest.fixture(scope="function")
+def trades_history_df(trades_history):
+    trades = trades_list_to_df(trades_history)
+    trades['date'] = pd.to_datetime(trades['timestamp'], unit='ms', utc=True)
+    return trades
 
 
 @pytest.fixture(scope="function")

tests/data/test_converter.py

@@ -4,13 +4,14 @@ from pathlib import Path
 from shutil import copyfile
 
 import numpy as np
+import pandas as pd
 import pytest
 
 from freqtrade.configuration.timerange import TimeRange
 from freqtrade.data.converter import (convert_ohlcv_format, convert_trades_format,
                                       ohlcv_fill_up_missing_data, ohlcv_to_dataframe,
-                                      reduce_dataframe_footprint, trades_dict_to_list,
-                                      trades_remove_duplicates, trades_to_ohlcv, trim_dataframe)
+                                      reduce_dataframe_footprint, trades_df_remove_duplicates,
+                                      trades_dict_to_list, trades_to_ohlcv, trim_dataframe)
 from freqtrade.data.history import (get_timerange, load_data, load_pair_history,
                                     validate_backtest_data)
 from freqtrade.data.history.idatahandler import IDataHandler
@@ -34,26 +35,21 @@ def test_ohlcv_to_dataframe(ohlcv_history_list, caplog):
     assert log_has('Converting candle (OHLCV) data to dataframe for pair UNITTEST/BTC.', caplog)
 
 
-def test_trades_to_ohlcv(ohlcv_history_list, caplog):
+def test_trades_to_ohlcv(trades_history_df, caplog):
 
     caplog.set_level(logging.DEBUG)
     with pytest.raises(ValueError, match="Trade-list empty."):
-        trades_to_ohlcv([], '1m')
+        trades_to_ohlcv(pd.DataFrame(columns=trades_history_df.columns), '1m')
 
-    trades = [
-        [1570752011620, "13519807", None, "sell", 0.00141342, 23.0, 0.03250866],
-        [1570752011620, "13519808", None, "sell", 0.00141266, 54.0, 0.07628364],
-        [1570752017964, "13519809", None, "sell", 0.00141266, 8.0, 0.01130128]]
-
-    df = trades_to_ohlcv(trades, '1m')
+    df = trades_to_ohlcv(trades_history_df, '1m')
     assert not df.empty
     assert len(df) == 1
     assert 'open' in df.columns
     assert 'high' in df.columns
     assert 'low' in df.columns
     assert 'close' in df.columns
-    assert df.loc[:, 'high'][0] == 0.00141342
-    assert df.loc[:, 'low'][0] == 0.00141266
+    assert df.loc[:, 'high'][0] == 0.019627
+    assert df.loc[:, 'low'][0] == 0.019626
 
 
 def test_ohlcv_fill_up_missing_data(testdatadir, caplog):
@@ -302,13 +298,13 @@ def test_trim_dataframe(testdatadir) -> None:
     assert all(data_modify.iloc[0] == data.iloc[25])
 
 
-def test_trades_remove_duplicates(trades_history):
-    trades_history1 = trades_history * 3
-    assert len(trades_history1) == len(trades_history) * 3
-    res = trades_remove_duplicates(trades_history1)
-    assert len(res) == len(trades_history)
-    for i, t in enumerate(res):
-        assert t == trades_history[i]
+def test_trades_df_remove_duplicates(trades_history_df):
+    trades_history1 = pd.concat([trades_history_df, trades_history_df, trades_history_df]
+                                ).reset_index(drop=True)
+    assert len(trades_history1) == len(trades_history_df) * 3
+    res = trades_df_remove_duplicates(trades_history1)
+    assert len(res) == len(trades_history_df)
+    assert res.equals(trades_history_df)
 
 
 def test_trades_dict_to_list(fetch_trades_result):

tests/data/test_datahandler.py

@@ -6,7 +6,8 @@ from pathlib import Path
 from unittest.mock import MagicMock
 
 import pytest
-from pandas import DataFrame
+from pandas import DataFrame, Timestamp
+from pandas.testing import assert_frame_equal
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import AVAILABLE_DATAHANDLERS
@@ -117,12 +118,6 @@ def test_datahandler_ohlcv_get_available_data(testdatadir):
     assert set(paircombs) == {('UNITTEST/BTC', '5m', CandleType.SPOT)}
 
 
-def test_jsondatahandler_trades_get_pairs(testdatadir):
-    pairs = JsonGzDataHandler.trades_get_pairs(testdatadir)
-    # Convert to set to avoid failures due to sorting
-    assert set(pairs) == {'XRP/ETH', 'XRP/OLD'}
-
-
 def test_jsondatahandler_ohlcv_purge(mocker, testdatadir):
     mocker.patch.object(Path, "exists", MagicMock(return_value=False))
     unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
@@ -246,8 +241,10 @@ def test_datahandler__check_empty_df(testdatadir, caplog):
     assert log_has_re(expected_text, caplog)
 
 
-@pytest.mark.parametrize('datahandler', ['parquet'])
+# @pytest.mark.parametrize('datahandler', [])
+@pytest.mark.skip("All datahandlers currently support trades data.")
 def test_datahandler_trades_not_supported(datahandler, testdatadir, ):
+    # Currently disabled. Reenable should a new provider not support trades data.
     dh = get_datahandler(testdatadir, datahandler)
     with pytest.raises(NotImplementedError):
         dh.trades_load('UNITTEST/ETH')
@@ -266,18 +263,6 @@ def test_jsondatahandler_trades_load(testdatadir, caplog):
     assert log_has(logmsg, caplog)
 
 
-def test_jsondatahandler_trades_purge(mocker, testdatadir):
-    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
-    unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
-    dh = JsonGzDataHandler(testdatadir)
-    assert not dh.trades_purge('UNITTEST/NONEXIST')
-    assert unlinkmock.call_count == 0
-
-    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
-    assert dh.trades_purge('UNITTEST/NONEXIST')
-    assert unlinkmock.call_count == 1
-
-
 @pytest.mark.parametrize('datahandler', AVAILABLE_DATAHANDLERS)
 def test_datahandler_ohlcv_append(datahandler, testdatadir, ):
     dh = get_datahandler(testdatadir, datahandler)
@@ -291,79 +276,48 @@ def test_datahandler_ohlcv_append(datahandler, testdatadir, ):
 def test_datahandler_trades_append(datahandler, testdatadir):
     dh = get_datahandler(testdatadir, datahandler)
     with pytest.raises(NotImplementedError):
-        dh.trades_append('UNITTEST/ETH', [])
+        dh.trades_append('UNITTEST/ETH', DataFrame())
 
 
-def test_hdf5datahandler_trades_get_pairs(testdatadir):
-    pairs = HDF5DataHandler.trades_get_pairs(testdatadir)
+@pytest.mark.parametrize('datahandler,expected', [
+    ('jsongz', {'XRP/ETH', 'XRP/OLD'}),
+    ('hdf5',  {'XRP/ETH'}),
+    ('feather', {'XRP/ETH'}),
+    ('parquet', {'XRP/ETH'}),
+])
+def test_datahandler_trades_get_pairs(testdatadir, datahandler, expected):
+
+    pairs = get_datahandlerclass(datahandler).trades_get_pairs(testdatadir)
     # Convert to set to avoid failures due to sorting
-    assert set(pairs) == {'XRP/ETH'}
+    assert set(pairs) == expected
 
 
 def test_hdf5datahandler_trades_load(testdatadir):
     dh = get_datahandler(testdatadir, 'hdf5')
     trades = dh.trades_load('XRP/ETH')
-    assert isinstance(trades, list)
+    assert isinstance(trades, DataFrame)
 
     trades1 = dh.trades_load('UNITTEST/NONEXIST')
-    assert trades1 == []
+    assert isinstance(trades1, DataFrame)
+    assert trades1.empty
     # data goes from 2019-10-11 - 2019-10-13
     timerange = TimeRange.parse_timerange('20191011-20191012')
 
     trades2 = dh._trades_load('XRP/ETH', timerange)
     assert len(trades) > len(trades2)
     # Check that ID is None (If it's nan, it's wrong)
-    assert trades2[0][2] is None
+    assert trades2.iloc[0]['type'] is None
 
     # unfiltered load has trades before starttime
-    assert len([t for t in trades if t[0] < timerange.startts * 1000]) >= 0
+
+    assert len(trades.loc[trades['timestamp'] < timerange.startts * 1000]) >= 0
     # filtered list does not have trades before starttime
-    assert len([t for t in trades2 if t[0] < timerange.startts * 1000]) == 0
+    assert len(trades2.loc[trades2['timestamp'] < timerange.startts * 1000]) == 0
     # unfiltered load has trades after endtime
-    assert len([t for t in trades if t[0] > timerange.stopts * 1000]) > 0
+    assert len(trades.loc[trades['timestamp'] > timerange.stopts * 1000]) >= 0
     # filtered list does not have trades after endtime
-    assert len([t for t in trades2 if t[0] > timerange.stopts * 1000]) == 0
-
-
-def test_hdf5datahandler_trades_store(testdatadir, tmpdir):
-    tmpdir1 = Path(tmpdir)
-    dh = get_datahandler(testdatadir, 'hdf5')
-    trades = dh.trades_load('XRP/ETH')
-
-    dh1 = get_datahandler(tmpdir1, 'hdf5')
-    dh1.trades_store('XRP/NEW', trades)
-    file = tmpdir1 / 'XRP_NEW-trades.h5'
-    assert file.is_file()
-    # Load trades back
-    trades_new = dh1.trades_load('XRP/NEW')
-
-    assert len(trades_new) == len(trades)
-    assert trades[0][0] == trades_new[0][0]
-    assert trades[0][1] == trades_new[0][1]
-    # assert trades[0][2] == trades_new[0][2]  # This is nan - so comparison does not make sense
-    assert trades[0][3] == trades_new[0][3]
-    assert trades[0][4] == trades_new[0][4]
-    assert trades[0][5] == trades_new[0][5]
-    assert trades[0][6] == trades_new[0][6]
-    assert trades[-1][0] == trades_new[-1][0]
-    assert trades[-1][1] == trades_new[-1][1]
-    # assert trades[-1][2] == trades_new[-1][2]  # This is nan - so comparison does not make sense
-    assert trades[-1][3] == trades_new[-1][3]
-    assert trades[-1][4] == trades_new[-1][4]
-    assert trades[-1][5] == trades_new[-1][5]
-    assert trades[-1][6] == trades_new[-1][6]
-
-
-def test_hdf5datahandler_trades_purge(mocker, testdatadir):
-    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
-    unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
-    dh = get_datahandler(testdatadir, 'hdf5')
-    assert not dh.trades_purge('UNITTEST/NONEXIST')
-    assert unlinkmock.call_count == 0
-
-    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
-    assert dh.trades_purge('UNITTEST/NONEXIST')
-    assert unlinkmock.call_count == 1
+    assert len(trades2.loc[trades2['timestamp'] > timerange.stopts * 1000]) == 0
+    # assert len([t for t in trades2 if t[0] > timerange.stopts * 1000]) == 0
 
 
 @pytest.mark.parametrize('pair,timeframe,candle_type,candle_append,startdt,enddt', [
@@ -490,50 +444,42 @@ def test_hdf5datahandler_ohlcv_purge(mocker, testdatadir):
     assert unlinkmock.call_count == 2
 
 
-def test_featherdatahandler_trades_load(testdatadir):
-    dh = get_datahandler(testdatadir, 'feather')
+@pytest.mark.parametrize('datahandler', ['jsongz', 'hdf5', 'feather', 'parquet'])
+def test_datahandler_trades_load(testdatadir, datahandler):
+    dh = get_datahandler(testdatadir, datahandler)
     trades = dh.trades_load('XRP/ETH')
-    assert isinstance(trades, list)
-    assert trades[0][0] == 1570752011620
-    assert trades[-1][-1] == 0.1986231
+    assert isinstance(trades, DataFrame)
+    assert trades.iloc[0]['timestamp'] == 1570752011620
+    assert trades.iloc[0]['date'] == Timestamp('2019-10-11 00:00:11.620000+0000')
+    assert trades.iloc[-1]['cost'] == 0.1986231
 
     trades1 = dh.trades_load('UNITTEST/NONEXIST')
-    assert trades1 == []
+    assert isinstance(trades, DataFrame)
+    assert trades1.empty
 
 
-def test_featherdatahandler_trades_store(testdatadir, tmpdir):
+@pytest.mark.parametrize('datahandler', ['jsongz', 'hdf5', 'feather', 'parquet'])
+def test_datahandler_trades_store(testdatadir, tmpdir, datahandler):
     tmpdir1 = Path(tmpdir)
-    dh = get_datahandler(testdatadir, 'feather')
+    dh = get_datahandler(testdatadir, datahandler)
     trades = dh.trades_load('XRP/ETH')
 
-    dh1 = get_datahandler(tmpdir1, 'feather')
+    dh1 = get_datahandler(tmpdir1, datahandler)
     dh1.trades_store('XRP/NEW', trades)
-    file = tmpdir1 / 'XRP_NEW-trades.feather'
+
+    file = tmpdir1 / f'XRP_NEW-trades.{dh1._get_file_extension()}'
     assert file.is_file()
     # Load trades back
     trades_new = dh1.trades_load('XRP/NEW')
-
+    assert_frame_equal(trades, trades_new, check_exact=True)
     assert len(trades_new) == len(trades)
-    assert trades[0][0] == trades_new[0][0]
-    assert trades[0][1] == trades_new[0][1]
-    # assert trades[0][2] == trades_new[0][2]  # This is nan - so comparison does not make sense
-    assert trades[0][3] == trades_new[0][3]
-    assert trades[0][4] == trades_new[0][4]
-    assert trades[0][5] == trades_new[0][5]
-    assert trades[0][6] == trades_new[0][6]
-    assert trades[-1][0] == trades_new[-1][0]
-    assert trades[-1][1] == trades_new[-1][1]
-    # assert trades[-1][2] == trades_new[-1][2]  # This is nan - so comparison does not make sense
-    assert trades[-1][3] == trades_new[-1][3]
-    assert trades[-1][4] == trades_new[-1][4]
-    assert trades[-1][5] == trades_new[-1][5]
-    assert trades[-1][6] == trades_new[-1][6]
 
 
-def test_featherdatahandler_trades_purge(mocker, testdatadir):
+@pytest.mark.parametrize('datahandler', ['jsongz', 'hdf5', 'feather', 'parquet'])
+def test_datahandler_trades_purge(mocker, testdatadir, datahandler):
     mocker.patch.object(Path, "exists", MagicMock(return_value=False))
     unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
-    dh = get_datahandler(testdatadir, 'feather')
+    dh = get_datahandler(testdatadir, datahandler)
     assert not dh.trades_purge('UNITTEST/NONEXIST')
     assert unlinkmock.call_count == 0
 

tests/data/test_dataprovider.py

@@ -129,9 +129,14 @@ def test_get_pair_dataframe(mocker, default_conf, ohlcv_history, candle_type):
     default_conf["runmode"] = RunMode.BACKTEST
     dp = DataProvider(default_conf, exchange)
     assert dp.runmode == RunMode.BACKTEST
-    assert isinstance(dp.get_pair_dataframe(
-        "UNITTEST/BTC", timeframe, candle_type=candle_type), DataFrame)
-    # assert dp.get_pair_dataframe("NONESENSE/AAA", timeframe).empty
+    df = dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)
+    assert isinstance(df, DataFrame)
+    assert len(df) == 3  # ohlcv_history mock has just 3 rows
+
+    dp._set_dataframe_max_date(ohlcv_history.iloc[-1]['date'])
+    df = dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)
+    assert isinstance(df, DataFrame)
+    assert len(df) == 2  # ohlcv_history is limited to 2 rows now
 
 
 def test_available_pairs(mocker, default_conf, ohlcv_history):
@@ -259,7 +264,7 @@ def test_orderbook(mocker, default_conf, order_book_l2):
     assert order_book_l2.call_args_list[0][0][0] == 'ETH/BTC'
     assert order_book_l2.call_args_list[0][0][1] >= 5
 
-    assert type(res) is dict
+    assert isinstance(res, dict)
     assert 'bids' in res
     assert 'asks' in res
 
@@ -272,7 +277,7 @@ def test_market(mocker, default_conf, markets):
     dp = DataProvider(default_conf, exchange)
     res = dp.market('ETH/BTC')
 
-    assert type(res) is dict
+    assert isinstance(res, dict)
     assert 'symbol' in res
     assert res['symbol'] == 'ETH/BTC'
 
@@ -286,7 +291,7 @@ def test_ticker(mocker, default_conf, tickers):
     exchange = get_patched_exchange(mocker, default_conf)
     dp = DataProvider(default_conf, exchange)
     res = dp.ticker('ETH/BTC')
-    assert type(res) is dict
+    assert isinstance(res, dict)
     assert 'symbol' in res
     assert res['symbol'] == 'ETH/BTC'
 

tests/data/test_history.py

@@ -3,6 +3,7 @@
 import json
 import logging
 import uuid
+from datetime import timedelta
 from pathlib import Path
 from shutil import copyfile
 from unittest.mock import MagicMock, PropertyMock
@@ -26,7 +27,7 @@ from freqtrade.enums import CandleType
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import file_dump_json
 from freqtrade.resolvers import StrategyResolver
-from freqtrade.util import dt_utc
+from freqtrade.util import dt_ts, dt_utc
 from tests.conftest import (CURRENT_TEST_STRATEGY, EXMS, get_patched_exchange, log_has, log_has_re,
                             patch_exchange)
 
@@ -569,7 +570,10 @@ def test_refresh_backtest_trades_data(mocker, default_conf, markets, caplog, tes
 
 
 def test_download_trades_history(trades_history, mocker, default_conf, testdatadir, caplog,
-                                 tmpdir) -> None:
+                                 tmpdir, time_machine) -> None:
+    start_dt = dt_utc(2023, 1, 1)
+    time_machine.move_to(start_dt, tick=False)
+
     tmpdir1 = Path(tmpdir)
     ght_mock = MagicMock(side_effect=lambda pair, *args, **kwargs: (pair, trades_history))
     mocker.patch(f'{EXMS}.get_historic_trades', ght_mock)
@@ -581,8 +585,13 @@ def test_download_trades_history(trades_history, mocker, default_conf, testdatad
 
     assert _download_trades_history(data_handler=data_handler, exchange=exchange,
                                     pair='ETH/BTC')
-    assert log_has("New Amount of trades: 5", caplog)
+    assert log_has("Current Amount of trades: 0", caplog)
+    assert log_has("New Amount of trades: 6", caplog)
+    assert ght_mock.call_count == 1
+    # Default "since" - 30 days before current day.
+    assert ght_mock.call_args_list[0][1]['since'] == dt_ts(start_dt - timedelta(days=30))
     assert file1.is_file()
+    caplog.clear()
 
     ght_mock.reset_mock()
     since_time = int(trades_history[-3][0] // 1000)
@@ -599,6 +608,7 @@ def test_download_trades_history(trades_history, mocker, default_conf, testdatad
     file1.unlink()
 
     mocker.patch(f'{EXMS}.get_historic_trades', MagicMock(side_effect=ValueError))
+    caplog.clear()
 
     assert not _download_trades_history(data_handler=data_handler, exchange=exchange,
                                         pair='ETH/BTC')
@@ -620,7 +630,7 @@ def test_download_trades_history(trades_history, mocker, default_conf, testdatad
 
     assert int(ght_mock.call_args_list[0][1]['since'] // 1000) == since_time
     assert ght_mock.call_args_list[0][1]['from_id'] is None
-    assert log_has_re(r'Start earlier than available data. Redownloading trades for.*', caplog)
+    assert log_has_re(r'Start .* earlier than available data. Redownloading trades for.*', caplog)
     _clean_test_file(file2)
 
 
@@ -651,10 +661,10 @@ def test_convert_trades_to_ohlcv(testdatadir, tmpdir, caplog):
 
     assert_frame_equal(dfbak_1m, df_1m, check_exact=True)
     assert_frame_equal(dfbak_5m, df_5m, check_exact=True)
-
-    assert not log_has('Could not convert NoDatapair to OHLCV.', caplog)
+    msg = 'Could not convert NoDatapair to OHLCV.'
+    assert not log_has(msg, caplog)
 
     convert_trades_to_ohlcv(['NoDatapair'], timeframes=['1m', '5m'],
                             data_format_trades='jsongz',
                             datadir=tmpdir1, timerange=tr, erase=True)
-    assert log_has('Could not convert NoDatapair to OHLCV.', caplog)
+    assert log_has(msg, caplog)

tests/exchange/test_binance.py

@@ -35,7 +35,7 @@ def test__get_params_binance(default_conf, mocker, side, type, time_in_force, ex
 ])
 def test_create_stoploss_order_binance(default_conf, mocker, limitratio, expected, side, trademode):
     api_mock = MagicMock()
-    order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_buy_{randint(0, 10 ** 6)}'
     order_type = 'stop_loss_limit' if trademode == TradingMode.SPOT else 'stop'
 
     api_mock.create_order = MagicMock(return_value={

tests/exchange/test_exchange.py

@@ -556,41 +556,6 @@ def test_get_min_pair_stake_amount_real_data(mocker, default_conf) -> None:
     assert result == 4000
 
 
-def test_set_sandbox(default_conf, mocker):
-    """
-    Test working scenario
-    """
-    api_mock = MagicMock()
-    api_mock.load_markets = MagicMock(return_value={
-        'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
-    })
-    url_mock = PropertyMock(return_value={'test': "api-public.sandbox.gdax.com",
-                                          'api': 'https://api.gdax.com'})
-    type(api_mock).urls = url_mock
-    exchange = get_patched_exchange(mocker, default_conf, api_mock)
-    liveurl = exchange._api.urls['api']
-    default_conf['exchange']['sandbox'] = True
-    exchange.set_sandbox(exchange._api, default_conf['exchange'], 'Logname')
-    assert exchange._api.urls['api'] != liveurl
-
-
-def test_set_sandbox_exception(default_conf, mocker):
-    """
-    Test Fail scenario
-    """
-    api_mock = MagicMock()
-    api_mock.load_markets = MagicMock(return_value={
-        'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
-    })
-    url_mock = PropertyMock(return_value={'api': 'https://api.gdax.com'})
-    type(api_mock).urls = url_mock
-
-    with pytest.raises(OperationalException, match=r'does not provide a sandbox api'):
-        exchange = get_patched_exchange(mocker, default_conf, api_mock)
-        default_conf['exchange']['sandbox'] = True
-        exchange.set_sandbox(exchange._api, default_conf['exchange'], 'Logname')
-
-
 def test__load_async_markets(default_conf, mocker, caplog):
     mocker.patch(f'{EXMS}._init_ccxt')
     mocker.patch(f'{EXMS}.validate_pairs')
@@ -1372,7 +1337,7 @@ def test_create_dry_run_order_market_fill(default_conf, mocker, side, rate, amou
 @pytest.mark.parametrize("exchange_name", EXCHANGES)
 def test_create_order(default_conf, mocker, side, ordertype, rate, marketprice, exchange_name):
     api_mock = MagicMock()
-    order_id = 'test_prod_{}_{}'.format(side, randint(0, 10 ** 6))
+    order_id = f'test_prod_{side}_{randint(0, 10 ** 6)}'
     api_mock.options = {} if not marketprice else {"createMarketBuyOrderRequiresPrice": True}
     api_mock.create_order = MagicMock(return_value={
         'id': order_id,
@@ -1452,7 +1417,7 @@ def test_buy_dry_run(default_conf, mocker, exchange_name):
 @pytest.mark.parametrize("exchange_name", EXCHANGES)
 def test_buy_prod(default_conf, mocker, exchange_name):
     api_mock = MagicMock()
-    order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_buy_{randint(0, 10 ** 6)}'
     order_type = 'market'
     time_in_force = 'gtc'
     api_mock.options = {}
@@ -1541,7 +1506,7 @@ def test_buy_prod(default_conf, mocker, exchange_name):
 @pytest.mark.parametrize("exchange_name", EXCHANGES)
 def test_buy_considers_time_in_force(default_conf, mocker, exchange_name):
     api_mock = MagicMock()
-    order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_buy_{randint(0, 10 ** 6)}'
     api_mock.options = {}
     api_mock.create_order = MagicMock(return_value={
         'id': order_id,
@@ -1608,7 +1573,7 @@ def test_sell_dry_run(default_conf, mocker):
 @pytest.mark.parametrize("exchange_name", EXCHANGES)
 def test_sell_prod(default_conf, mocker, exchange_name):
     api_mock = MagicMock()
-    order_id = 'test_prod_sell_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_sell_{randint(0, 10 ** 6)}'
     order_type = 'market'
     api_mock.options = {}
     api_mock.create_order = MagicMock(return_value={
@@ -1686,7 +1651,7 @@ def test_sell_prod(default_conf, mocker, exchange_name):
 @pytest.mark.parametrize("exchange_name", EXCHANGES)
 def test_sell_considers_time_in_force(default_conf, mocker, exchange_name):
     api_mock = MagicMock()
-    order_id = 'test_prod_sell_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_sell_{randint(0, 10 ** 6)}'
     api_mock.create_order = MagicMock(return_value={
         'id': order_id,
         'symbol': 'ETH/BTC',
@@ -2505,7 +2470,7 @@ def test_refresh_latest_ohlcv_inv_result(default_conf, mocker, caplog):
     assert exchange._klines
     assert exchange._api_async.fetch_ohlcv.call_count == 2
 
-    assert type(res) is dict
+    assert isinstance(res, dict)
     assert len(res) == 1
     # Test that each is in list at least once as order is not guaranteed
     assert log_has("Error loading ETH/BTC. Result was [[]].", caplog)
@@ -2889,7 +2854,7 @@ async def test__async_fetch_trades(default_conf, mocker, caplog, exchange_name,
 
     pair = 'ETH/BTC'
     res = await exchange._async_fetch_trades(pair, since=None, params=None)
-    assert type(res) is list
+    assert isinstance(res, list)
     assert isinstance(res[0], list)
     assert isinstance(res[1], list)
 
@@ -2989,9 +2954,9 @@ async def test__async_get_trade_history_id(default_conf, mocker, exchange_name,
     ret = await exchange._async_get_trade_history_id(pair,
                                                      since=fetch_trades_result[0]['timestamp'],
                                                      until=fetch_trades_result[-1]['timestamp'] - 1)
-    assert type(ret) is tuple
+    assert isinstance(ret, tuple)
     assert ret[0] == pair
-    assert type(ret[1]) is list
+    assert isinstance(ret[1], list)
     assert len(ret[1]) == len(fetch_trades_result)
     assert exchange._api_async.fetch_trades.call_count == 3
     fetch_trades_cal = exchange._api_async.fetch_trades.call_args_list
@@ -3027,9 +2992,9 @@ async def test__async_get_trade_history_time(default_conf, mocker, caplog, excha
         pair,
         since=fetch_trades_result[0]['timestamp'],
         until=fetch_trades_result[-1]['timestamp'] - 1)
-    assert type(ret) is tuple
+    assert isinstance(ret, tuple)
     assert ret[0] == pair
-    assert type(ret[1]) is list
+    assert isinstance(ret[1], list)
     assert len(ret[1]) == len(fetch_trades_result)
     assert exchange._api_async.fetch_trades.call_count == 2
     fetch_trades_cal = exchange._api_async.fetch_trades.call_args_list
@@ -3063,9 +3028,9 @@ async def test__async_get_trade_history_time_empty(default_conf, mocker, caplog,
     pair = 'ETH/BTC'
     ret = await exchange._async_get_trade_history_time(pair, since=trades_history[0][0],
                                                        until=trades_history[-1][0] - 1)
-    assert type(ret) is tuple
+    assert isinstance(ret, tuple)
     assert ret[0] == pair
-    assert type(ret[1]) is list
+    assert isinstance(ret[1], list)
     assert len(ret[1]) == len(trades_history) - 1
     assert exchange._async_fetch_trades.call_count == 2
     fetch_trades_cal = exchange._async_fetch_trades.call_args_list
@@ -3557,7 +3522,7 @@ def test_get_valid_pair_combination(default_conf, mocker, markets):
 
     assert ex.get_valid_pair_combination("ETH", "BTC") == "ETH/BTC"
     assert ex.get_valid_pair_combination("BTC", "ETH") == "ETH/BTC"
-    with pytest.raises(DependencyException, match=r"Could not combine.* to get a valid pair."):
+    with pytest.raises(ValueError, match=r"Could not combine.* to get a valid pair."):
         ex.get_valid_pair_combination("NOPAIR", "ETH")
 
 
@@ -5392,7 +5357,7 @@ def test_get_liquidation_price(
 ])
 def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amount):
     api_mock = MagicMock()
-    order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_buy_{randint(0, 10 ** 6)}'
 
     api_mock.create_order = MagicMock(return_value={
         'id': order_id,

tests/exchange/test_huobi.py

@@ -16,7 +16,7 @@ from tests.exchange.test_exchange import ccxt_exceptionhandlers
 ])
 def test_create_stoploss_order_huobi(default_conf, mocker, limitratio, expected, side):
     api_mock = MagicMock()
-    order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_buy_{randint(0, 10 ** 6)}'
     order_type = 'stop-limit'
 
     api_mock.create_order = MagicMock(return_value={

tests/exchange/test_kraken.py

@@ -15,7 +15,7 @@ STOPLOSS_LIMIT_ORDERTYPE = 'stop-loss-limit'
 
 def test_buy_kraken_trading_agreement(default_conf, mocker):
     api_mock = MagicMock()
-    order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_buy_{randint(0, 10 ** 6)}'
     order_type = 'limit'
     time_in_force = 'ioc'
     api_mock.options = {}
@@ -56,7 +56,7 @@ def test_buy_kraken_trading_agreement(default_conf, mocker):
 
 def test_sell_kraken_trading_agreement(default_conf, mocker):
     api_mock = MagicMock()
-    order_id = 'test_prod_sell_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_sell_{randint(0, 10 ** 6)}'
     order_type = 'market'
     api_mock.options = {}
     api_mock.create_order = MagicMock(return_value={
@@ -181,7 +181,7 @@ def test_get_balances_prod(default_conf, mocker):
 ])
 def test_create_stoploss_order_kraken(default_conf, mocker, ordertype, side, adjustedprice):
     api_mock = MagicMock()
-    order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_buy_{randint(0, 10 ** 6)}'
 
     api_mock.create_order = MagicMock(return_value={
         'id': order_id,

tests/exchange/test_kucoin.py

@@ -17,7 +17,7 @@ from tests.exchange.test_exchange import ccxt_exceptionhandlers
 ])
 def test_create_stoploss_order_kucoin(default_conf, mocker, limitratio, expected, side, order_type):
     api_mock = MagicMock()
-    order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
+    order_id = f'test_prod_buy_{randint(0, 10 ** 6)}'
 
     api_mock.create_order = MagicMock(return_value={
         'id': order_id,
@@ -136,7 +136,7 @@ def test_stoploss_adjust_kucoin(mocker, default_conf):
 ])
 def test_kucoin_create_order(default_conf, mocker, side, ordertype, rate):
     api_mock = MagicMock()
-    order_id = 'test_prod_{}_{}'.format(side, randint(0, 10 ** 6))
+    order_id = f'test_prod_{side}_{randint(0, 10 ** 6)}'
     api_mock.create_order = MagicMock(return_value={
         'id': order_id,
         'info': {

tests/exchange_online/conftest.py

@@ -0,0 +1,334 @@
+from copy import deepcopy
+from pathlib import Path
+from typing import Tuple
+
+import pytest
+
+from freqtrade.constants import Config
+from freqtrade.exchange.exchange import Exchange
+from freqtrade.resolvers.exchange_resolver import ExchangeResolver
+from tests.conftest import EXMS, get_default_conf_usdt
+
+
+EXCHANGE_FIXTURE_TYPE = Tuple[Exchange, str]
+
+# Exchanges that should be tested online
+EXCHANGES = {
+    'bittrex': {
+        'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
+        'hasQuoteVolume': False,
+        'timeframe': '1h',
+        'leverage_tiers_public': False,
+        'leverage_in_spot_market': False,
+    },
+    'binance': {
+        'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
+        'use_ci_proxy': True,
+        'hasQuoteVolume': True,
+        'timeframe': '1h',
+        'futures': True,
+        'futures_pair': 'BTC/USDT:USDT',
+        'hasQuoteVolumeFutures': True,
+        'leverage_tiers_public': False,
+        'leverage_in_spot_market': False,
+        'trades_lookback_hours': 4,
+        'private_methods': [
+            'fapiPrivateGetPositionSideDual',
+            'fapiPrivateGetMultiAssetsMargin'
+        ],
+        'sample_order': [{
+            "symbol": "SOLUSDT",
+            "orderId": 3551312894,
+            "orderListId": -1,
+            "clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
+            "transactTime": 1674493798550,
+            "price": "15.50000000",
+            "origQty": "1.10000000",
+            "executedQty": "0.00000000",
+            "cummulativeQuoteQty": "0.00000000",
+            "status": "NEW",
+            "timeInForce": "GTC",
+            "type": "LIMIT",
+            "side": "BUY",
+            "workingTime": 1674493798550,
+            "fills": [],
+            "selfTradePreventionMode": "NONE",
+        }]
+    },
+    'binanceus': {
+        'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
+        'hasQuoteVolume': True,
+        'timeframe': '1h',
+        'futures': False,
+        'sample_order': [{
+            "symbol": "SOLUSDT",
+            "orderId": 3551312894,
+            "orderListId": -1,
+            "clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
+            "transactTime": 1674493798550,
+            "price": "15.50000000",
+            "origQty": "1.10000000",
+            "executedQty": "0.00000000",
+            "cummulativeQuoteQty": "0.00000000",
+            "status": "NEW",
+            "timeInForce": "GTC",
+            "type": "LIMIT",
+            "side": "BUY",
+            "workingTime": 1674493798550,
+            "fills": [],
+            "selfTradePreventionMode": "NONE",
+        }]
+    },
+    'kraken': {
+        'pair': 'BTC/USD',
+        'stake_currency': 'USD',
+        'hasQuoteVolume': True,
+        'timeframe': '1h',
+        'leverage_tiers_public': False,
+        'leverage_in_spot_market': True,
+        'trades_lookback_hours': 12,
+    },
+    'kucoin': {
+        'pair': 'XRP/USDT',
+        'stake_currency': 'USDT',
+        'hasQuoteVolume': True,
+        'timeframe': '1h',
+        'leverage_tiers_public': False,
+        'leverage_in_spot_market': True,
+        'sample_order': [
+            {'id': '63d6742d0adc5570001d2bbf7'},  # create order
+            {
+                'id': '63d6742d0adc5570001d2bbf7',
+                'symbol': 'SOL-USDT',
+                'opType': 'DEAL',
+                'type': 'limit',
+                'side': 'buy',
+                'price': '15.5',
+                'size': '1.1',
+                'funds': '0',
+                'dealFunds': '17.05',
+                'dealSize': '1.1',
+                'fee': '0.000065252',
+                'feeCurrency': 'USDT',
+                'stp': '',
+                'stop': '',
+                'stopTriggered': False,
+                'stopPrice': '0',
+                'timeInForce': 'GTC',
+                'postOnly': False,
+                'hidden': False,
+                'iceberg': False,
+                'visibleSize': '0',
+                'cancelAfter': 0,
+                'channel': 'API',
+                'clientOid': '0a053870-11bf-41e5-be61-b272a4cb62e1',
+                'remark': None,
+                'tags': 'partner:ccxt',
+                'isActive': False,
+                'cancelExist': False,
+                'createdAt': 1674493798550,
+                'tradeType': 'TRADE'
+            }],
+    },
+    'gate': {
+        'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
+        'hasQuoteVolume': True,
+        'timeframe': '1h',
+        'futures': True,
+        'futures_pair': 'BTC/USDT:USDT',
+        'hasQuoteVolumeFutures': True,
+        'leverage_tiers_public': True,
+        'leverage_in_spot_market': True,
+        'sample_order': [
+            {
+                "id": "276266139423",
+                "text": "apiv4",
+                "create_time": "1674493798",
+                "update_time": "1674493798",
+                "create_time_ms": "1674493798550",
+                "update_time_ms": "1674493798550",
+                "status": "closed",
+                "currency_pair": "SOL_USDT",
+                "type": "limit",
+                "account": "spot",
+                "side": "buy",
+                "amount": "1.1",
+                "price": "15.5",
+                "time_in_force": "gtc",
+                "iceberg": "0",
+                "left": "0",
+                "fill_price": "17.05",
+                "filled_total": "17.05",
+                "avg_deal_price": "15.5",
+                "fee": "0.0000018",
+                "fee_currency": "SOL",
+                "point_fee": "0",
+                "gt_fee": "0",
+                "gt_maker_fee": "0",
+                "gt_taker_fee": "0.0015",
+                "gt_discount": True,
+                "rebated_fee": "0",
+                "rebated_fee_currency": "USDT"
+            },
+            {
+                # market order
+                'id': '276401180529',
+                'text': 'apiv4',
+                'create_time': '1674493798',
+                'update_time': '1674493798',
+                'create_time_ms': '1674493798550',
+                'update_time_ms': '1674493798550',
+                'status': 'cancelled',
+                'currency_pair': 'SOL_USDT',
+                'type': 'market',
+                'account': 'spot',
+                'side': 'buy',
+                'amount': '17.05',
+                'price': '0',
+                'time_in_force': 'ioc',
+                'iceberg': '0',
+                'left': '0.0000000016228',
+                'fill_price': '17.05',
+                'filled_total': '17.05',
+                'avg_deal_price': '15.5',
+                'fee': '0',
+                'fee_currency': 'SOL',
+                'point_fee': '0.0199999999967544',
+                'gt_fee': '0',
+                'gt_maker_fee': '0',
+                'gt_taker_fee': '0',
+                'gt_discount': False,
+                'rebated_fee': '0',
+                'rebated_fee_currency': 'USDT'
+            }
+        ],
+    },
+    'okx': {
+        'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
+        'hasQuoteVolume': True,
+        'timeframe': '1h',
+        'futures': True,
+        'futures_pair': 'BTC/USDT:USDT',
+        'hasQuoteVolumeFutures': False,
+        'leverage_tiers_public': True,
+        'leverage_in_spot_market': True,
+        'private_methods': ['fetch_accounts'],
+    },
+    'bybit': {
+        'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
+        'hasQuoteVolume': True,
+        'use_ci_proxy': True,
+        'timeframe': '1h',
+        'futures_pair': 'BTC/USDT:USDT',
+        'futures': True,
+        'leverage_tiers_public': True,
+        'leverage_in_spot_market': True,
+        'sample_order': [
+            {
+                "orderId": "1274754916287346280",
+                "orderLinkId": "1666798627015730",
+                "symbol": "SOLUSDT",
+                "createTime": "1674493798550",
+                "orderPrice": "15.5",
+                "orderQty": "1.1",
+                "orderType": "LIMIT",
+                "side": "BUY",
+                "status": "NEW",
+                "timeInForce": "GTC",
+                "accountId": "5555555",
+                "execQty": "0",
+                "orderCategory": "0"
+            }
+        ]
+    },
+    'huobi': {
+        'pair': 'ETH/BTC',
+        'stake_currency': 'BTC',
+        'hasQuoteVolume': True,
+        'timeframe': '1h',
+        'futures': False,
+    },
+    'bitvavo': {
+        'pair': 'BTC/EUR',
+        'stake_currency': 'EUR',
+        'hasQuoteVolume': True,
+        'timeframe': '1h',
+        'leverage_tiers_public': False,
+        'leverage_in_spot_market': False,
+    },
+}
+
+
+@pytest.fixture(scope="class")
+def exchange_conf():
+    config = get_default_conf_usdt((Path(__file__).parent / "testdata").resolve())
+    config['exchange']['pair_whitelist'] = []
+    config['exchange']['key'] = ''
+    config['exchange']['secret'] = ''
+    config['dry_run'] = False
+    config['entry_pricing']['use_order_book'] = True
+    config['exit_pricing']['use_order_book'] = True
+    return config
+
+
+def set_test_proxy(config: Config, use_proxy: bool) -> Config:
+    # Set proxy to test in CI.
+    import os
+    if use_proxy and (proxy := os.environ.get('CI_WEB_PROXY')):
+        config1 = deepcopy(config)
+        config1['exchange']['ccxt_config'] = {
+            "httpsProxy": proxy,
+        }
+        return config1
+
+    return config
+
+
+def get_exchange(exchange_name, exchange_conf):
+    exchange_conf = set_test_proxy(
+        exchange_conf, EXCHANGES[exchange_name].get('use_ci_proxy', False))
+    exchange_conf['exchange']['name'] = exchange_name
+    exchange_conf['stake_currency'] = EXCHANGES[exchange_name]['stake_currency']
+    exchange = ExchangeResolver.load_exchange(exchange_conf, validate=True,
+                                              load_leverage_tiers=True)
+
+    yield exchange, exchange_name
+
+
+def get_futures_exchange(exchange_name, exchange_conf, class_mocker):
+    if EXCHANGES[exchange_name].get('futures') is not True:
+        pytest.skip(f"Exchange {exchange_name} does not support futures.")
+    else:
+        exchange_conf = deepcopy(exchange_conf)
+        exchange_conf = set_test_proxy(
+            exchange_conf, EXCHANGES[exchange_name].get('use_ci_proxy', False))
+        exchange_conf['trading_mode'] = 'futures'
+        exchange_conf['margin_mode'] = 'isolated'
+
+        class_mocker.patch(
+            'freqtrade.exchange.binance.Binance.fill_leverage_tiers')
+        class_mocker.patch(f'{EXMS}.fetch_trading_fees')
+        class_mocker.patch('freqtrade.exchange.okx.Okx.additional_exchange_init')
+        class_mocker.patch('freqtrade.exchange.binance.Binance.additional_exchange_init')
+        class_mocker.patch('freqtrade.exchange.bybit.Bybit.additional_exchange_init')
+        class_mocker.patch(f'{EXMS}.load_cached_leverage_tiers', return_value=None)
+        class_mocker.patch(f'{EXMS}.cache_leverage_tiers')
+
+        yield from get_exchange(exchange_name, exchange_conf)
+
+
+@pytest.fixture(params=EXCHANGES, scope="class")
+def exchange(request, exchange_conf):
+    yield from get_exchange(request.param, exchange_conf)
+
+
+@pytest.fixture(params=EXCHANGES, scope="class")
+def exchange_futures(request, exchange_conf, class_mocker):
+
+    yield from get_futures_exchange(request.param, exchange_conf, class_mocker)

tests/exchange_online/test_ccxt_compat.py

@@ -5,338 +5,14 @@ However, these tests should give a good idea to determine if a new exchange is
 suitable to run with freqtrade.
 """
 
-from copy import deepcopy
 from datetime import datetime, timedelta, timezone
-from pathlib import Path
-from typing import Tuple
 
 import pytest
 
-from freqtrade.constants import Config
 from freqtrade.enums import CandleType
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
-from freqtrade.exchange.exchange import Exchange, timeframe_to_msecs
-from freqtrade.resolvers.exchange_resolver import ExchangeResolver
-from tests.conftest import EXMS, get_default_conf_usdt
-
-
-EXCHANGE_FIXTURE_TYPE = Tuple[Exchange, str]
-
-# Exchanges that should be tested
-EXCHANGES = {
-    'bittrex': {
-        'pair': 'BTC/USDT',
-        'stake_currency': 'USDT',
-        'hasQuoteVolume': False,
-        'timeframe': '1h',
-        'leverage_tiers_public': False,
-        'leverage_in_spot_market': False,
-    },
-    'binance': {
-        'pair': 'BTC/USDT',
-        'stake_currency': 'USDT',
-        'use_ci_proxy': True,
-        'hasQuoteVolume': True,
-        'timeframe': '1h',
-        'futures': True,
-        'futures_pair': 'BTC/USDT:USDT',
-        'hasQuoteVolumeFutures': True,
-        'leverage_tiers_public': False,
-        'leverage_in_spot_market': False,
-        'trades_lookback_hours': 4,
-        'private_methods': [
-            'fapiPrivateGetPositionSideDual',
-            'fapiPrivateGetMultiAssetsMargin'
-        ],
-        'sample_order': [{
-            "symbol": "SOLUSDT",
-            "orderId": 3551312894,
-            "orderListId": -1,
-            "clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
-            "transactTime": 1674493798550,
-            "price": "15.50000000",
-            "origQty": "1.10000000",
-            "executedQty": "0.00000000",
-            "cummulativeQuoteQty": "0.00000000",
-            "status": "NEW",
-            "timeInForce": "GTC",
-            "type": "LIMIT",
-            "side": "BUY",
-            "workingTime": 1674493798550,
-            "fills": [],
-            "selfTradePreventionMode": "NONE",
-        }]
-    },
-    'binanceus': {
-        'pair': 'BTC/USDT',
-        'stake_currency': 'USDT',
-        'hasQuoteVolume': True,
-        'timeframe': '1h',
-        'futures': False,
-        'sample_order': [{
-            "symbol": "SOLUSDT",
-            "orderId": 3551312894,
-            "orderListId": -1,
-            "clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
-            "transactTime": 1674493798550,
-            "price": "15.50000000",
-            "origQty": "1.10000000",
-            "executedQty": "0.00000000",
-            "cummulativeQuoteQty": "0.00000000",
-            "status": "NEW",
-            "timeInForce": "GTC",
-            "type": "LIMIT",
-            "side": "BUY",
-            "workingTime": 1674493798550,
-            "fills": [],
-            "selfTradePreventionMode": "NONE",
-        }]
-    },
-    'kraken': {
-        'pair': 'BTC/USD',
-        'stake_currency': 'USD',
-        'hasQuoteVolume': True,
-        'timeframe': '1h',
-        'leverage_tiers_public': False,
-        'leverage_in_spot_market': True,
-        'trades_lookback_hours': 12,
-    },
-    'kucoin': {
-        'pair': 'XRP/USDT',
-        'stake_currency': 'USDT',
-        'hasQuoteVolume': True,
-        'timeframe': '1h',
-        'leverage_tiers_public': False,
-        'leverage_in_spot_market': True,
-        'sample_order': [
-            {'id': '63d6742d0adc5570001d2bbf7'},  # create order
-            {
-                'id': '63d6742d0adc5570001d2bbf7',
-                'symbol': 'SOL-USDT',
-                'opType': 'DEAL',
-                'type': 'limit',
-                'side': 'buy',
-                'price': '15.5',
-                'size': '1.1',
-                'funds': '0',
-                'dealFunds': '17.05',
-                'dealSize': '1.1',
-                'fee': '0.000065252',
-                'feeCurrency': 'USDT',
-                'stp': '',
-                'stop': '',
-                'stopTriggered': False,
-                'stopPrice': '0',
-                'timeInForce': 'GTC',
-                'postOnly': False,
-                'hidden': False,
-                'iceberg': False,
-                'visibleSize': '0',
-                'cancelAfter': 0,
-                'channel': 'API',
-                'clientOid': '0a053870-11bf-41e5-be61-b272a4cb62e1',
-                'remark': None,
-                'tags': 'partner:ccxt',
-                'isActive': False,
-                'cancelExist': False,
-                'createdAt': 1674493798550,
-                'tradeType': 'TRADE'
-            }],
-    },
-    'gate': {
-        'pair': 'BTC/USDT',
-        'stake_currency': 'USDT',
-        'hasQuoteVolume': True,
-        'timeframe': '1h',
-        'futures': True,
-        'futures_pair': 'BTC/USDT:USDT',
-        'hasQuoteVolumeFutures': True,
-        'leverage_tiers_public': True,
-        'leverage_in_spot_market': True,
-        'sample_order': [
-            {
-                "id": "276266139423",
-                "text": "apiv4",
-                "create_time": "1674493798",
-                "update_time": "1674493798",
-                "create_time_ms": "1674493798550",
-                "update_time_ms": "1674493798550",
-                "status": "closed",
-                "currency_pair": "SOL_USDT",
-                "type": "limit",
-                "account": "spot",
-                "side": "buy",
-                "amount": "1.1",
-                "price": "15.5",
-                "time_in_force": "gtc",
-                "iceberg": "0",
-                "left": "0",
-                "fill_price": "17.05",
-                "filled_total": "17.05",
-                "avg_deal_price": "15.5",
-                "fee": "0.0000018",
-                "fee_currency": "SOL",
-                "point_fee": "0",
-                "gt_fee": "0",
-                "gt_maker_fee": "0",
-                "gt_taker_fee": "0.0015",
-                "gt_discount": True,
-                "rebated_fee": "0",
-                "rebated_fee_currency": "USDT"
-            },
-            {
-                # market order
-                'id': '276401180529',
-                'text': 'apiv4',
-                'create_time': '1674493798',
-                'update_time': '1674493798',
-                'create_time_ms': '1674493798550',
-                'update_time_ms': '1674493798550',
-                'status': 'cancelled',
-                'currency_pair': 'SOL_USDT',
-                'type': 'market',
-                'account': 'spot',
-                'side': 'buy',
-                'amount': '17.05',
-                'price': '0',
-                'time_in_force': 'ioc',
-                'iceberg': '0',
-                'left': '0.0000000016228',
-                'fill_price': '17.05',
-                'filled_total': '17.05',
-                'avg_deal_price': '15.5',
-                'fee': '0',
-                'fee_currency': 'SOL',
-                'point_fee': '0.0199999999967544',
-                'gt_fee': '0',
-                'gt_maker_fee': '0',
-                'gt_taker_fee': '0',
-                'gt_discount': False,
-                'rebated_fee': '0',
-                'rebated_fee_currency': 'USDT'
-            }
-        ],
-    },
-    'okx': {
-        'pair': 'BTC/USDT',
-        'stake_currency': 'USDT',
-        'hasQuoteVolume': True,
-        'timeframe': '1h',
-        'futures': True,
-        'futures_pair': 'BTC/USDT:USDT',
-        'hasQuoteVolumeFutures': False,
-        'leverage_tiers_public': True,
-        'leverage_in_spot_market': True,
-        'private_methods': ['fetch_accounts'],
-    },
-    'bybit': {
-        'pair': 'BTC/USDT',
-        'stake_currency': 'USDT',
-        'hasQuoteVolume': True,
-        'use_ci_proxy': True,
-        'timeframe': '1h',
-        'futures_pair': 'BTC/USDT:USDT',
-        'futures': True,
-        'leverage_tiers_public': True,
-        'leverage_in_spot_market': True,
-        'sample_order': [
-            {
-                "orderId": "1274754916287346280",
-                "orderLinkId": "1666798627015730",
-                "symbol": "SOLUSDT",
-                "createTime": "1674493798550",
-                "orderPrice": "15.5",
-                "orderQty": "1.1",
-                "orderType": "LIMIT",
-                "side": "BUY",
-                "status": "NEW",
-                "timeInForce": "GTC",
-                "accountId": "5555555",
-                "execQty": "0",
-                "orderCategory": "0"
-            }
-        ]
-    },
-    'huobi': {
-        'pair': 'ETH/BTC',
-        'stake_currency': 'BTC',
-        'hasQuoteVolume': True,
-        'timeframe': '1h',
-        'futures': False,
-    },
-    'bitvavo': {
-        'pair': 'BTC/EUR',
-        'stake_currency': 'EUR',
-        'hasQuoteVolume': True,
-        'timeframe': '1h',
-        'leverage_tiers_public': False,
-        'leverage_in_spot_market': False,
-    },
-}
-
-
-@pytest.fixture(scope="class")
-def exchange_conf():
-    config = get_default_conf_usdt((Path(__file__).parent / "testdata").resolve())
-    config['exchange']['pair_whitelist'] = []
-    config['exchange']['key'] = ''
-    config['exchange']['secret'] = ''
-    config['dry_run'] = False
-    config['entry_pricing']['use_order_book'] = True
-    config['exit_pricing']['use_order_book'] = True
-    return config
-
-
-def set_test_proxy(config: Config, use_proxy: bool) -> Config:
-    # Set proxy to test in CI.
-    import os
-    if use_proxy and (proxy := os.environ.get('CI_WEB_PROXY')):
-        config1 = deepcopy(config)
-        config1['exchange']['ccxt_config'] = {
-            "httpsProxy": proxy,
-        }
-        return config1
-
-    return config
-
-
-@pytest.fixture(params=EXCHANGES, scope="class")
-def exchange(request, exchange_conf):
-    exchange_conf = set_test_proxy(
-        exchange_conf, EXCHANGES[request.param].get('use_ci_proxy', False))
-    exchange_conf['exchange']['name'] = request.param
-    exchange_conf['stake_currency'] = EXCHANGES[request.param]['stake_currency']
-    exchange = ExchangeResolver.load_exchange(exchange_conf, validate=True)
-
-    yield exchange, request.param
-
-
-@pytest.fixture(params=EXCHANGES, scope="class")
-def exchange_futures(request, exchange_conf, class_mocker):
-    if EXCHANGES[request.param].get('futures') is not True:
-        yield None, request.param
-    else:
-        exchange_conf = set_test_proxy(
-            exchange_conf, EXCHANGES[request.param].get('use_ci_proxy', False))
-        exchange_conf = deepcopy(exchange_conf)
-        exchange_conf['exchange']['name'] = request.param
-        exchange_conf['trading_mode'] = 'futures'
-        exchange_conf['margin_mode'] = 'isolated'
-        exchange_conf['stake_currency'] = EXCHANGES[request.param]['stake_currency']
-
-        class_mocker.patch(
-            'freqtrade.exchange.binance.Binance.fill_leverage_tiers')
-        class_mocker.patch(f'{EXMS}.fetch_trading_fees')
-        class_mocker.patch('freqtrade.exchange.okx.Okx.additional_exchange_init')
-        class_mocker.patch('freqtrade.exchange.binance.Binance.additional_exchange_init')
-        class_mocker.patch('freqtrade.exchange.bybit.Bybit.additional_exchange_init')
-        class_mocker.patch(f'{EXMS}.load_cached_leverage_tiers', return_value=None)
-        class_mocker.patch(f'{EXMS}.cache_leverage_tiers')
-
-        exchange = ExchangeResolver.load_exchange(
-            exchange_conf, validate=True, load_leverage_tiers=True)
-
-        yield exchange, request.param
+from freqtrade.exchange.exchange import timeframe_to_msecs
+from tests.exchange_online.conftest import EXCHANGE_FIXTURE_TYPE, EXCHANGES
 
 
 @pytest.mark.longrun
@@ -371,9 +47,6 @@ class TestCCXTExchange:
 
     def test_load_markets_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
         exchange, exchangename = exchange_futures
-        if not exchange:
-            # exchange_futures only returns values for supported exchanges
-            return
         pair = EXCHANGES[exchangename]['pair']
         pair = EXCHANGES[exchangename].get('futures_pair', pair)
         markets = exchange.markets
@@ -561,9 +234,6 @@ class TestCCXTExchange:
     def test_ccxt__async_get_candle_history_futures(
             self, exchange_futures: EXCHANGE_FIXTURE_TYPE, candle_type):
         exchange, exchangename = exchange_futures
-        if not exchange:
-            # exchange_futures only returns values for supported exchanges
-            return
         pair = EXCHANGES[exchangename].get('futures_pair', EXCHANGES[exchangename]['pair'])
         timeframe = EXCHANGES[exchangename]['timeframe']
         if candle_type == CandleType.FUNDING_RATE:
@@ -579,9 +249,6 @@ class TestCCXTExchange:
 
     def test_ccxt_fetch_funding_rate_history(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
         exchange, exchangename = exchange_futures
-        if not exchange:
-            # exchange_futures only returns values for supported exchanges
-            return
 
         pair = EXCHANGES[exchangename].get('futures_pair', EXCHANGES[exchangename]['pair'])
         since = int((datetime.now(timezone.utc) - timedelta(days=5)).timestamp() * 1000)
@@ -617,9 +284,6 @@ class TestCCXTExchange:
 
     def test_ccxt_fetch_mark_price_history(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
         exchange, exchangename = exchange_futures
-        if not exchange:
-            # exchange_futures only returns values for supported exchanges
-            return
         pair = EXCHANGES[exchangename].get('futures_pair', EXCHANGES[exchangename]['pair'])
         since = int((datetime.now(timezone.utc) - timedelta(days=5)).timestamp() * 1000)
         pair_tf = (pair, '1h', CandleType.MARK)
@@ -641,9 +305,6 @@ class TestCCXTExchange:
 
     def test_ccxt__calculate_funding_fees(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
         exchange, exchangename = exchange_futures
-        if not exchange:
-            # exchange_futures only returns values for supported exchanges
-            return
         pair = EXCHANGES[exchangename].get('futures_pair', EXCHANGES[exchangename]['pair'])
         since = datetime.now(timezone.utc) - timedelta(days=5)
 
@@ -690,31 +351,29 @@ class TestCCXTExchange:
 
     def test_ccxt_get_max_leverage_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
         futures, futures_name = exchange_futures
-        if futures:
-            leverage_tiers_public = EXCHANGES[futures_name].get('leverage_tiers_public')
-            if leverage_tiers_public:
-                futures_pair = EXCHANGES[futures_name].get(
-                    'futures_pair',
-                    EXCHANGES[futures_name]['pair']
-                )
-                futures_leverage = futures.get_max_leverage(futures_pair, 20)
-                assert (isinstance(futures_leverage, float) or isinstance(futures_leverage, int))
-                assert futures_leverage >= 1.0
-
-    def test_ccxt_get_contract_size(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
-        futures, futures_name = exchange_futures
-        if futures:
+        leverage_tiers_public = EXCHANGES[futures_name].get('leverage_tiers_public')
+        if leverage_tiers_public:
             futures_pair = EXCHANGES[futures_name].get(
                 'futures_pair',
                 EXCHANGES[futures_name]['pair']
             )
-            contract_size = futures.get_contract_size(futures_pair)
-            assert (isinstance(contract_size, float) or isinstance(contract_size, int))
-            assert contract_size >= 0.0
+            futures_leverage = futures.get_max_leverage(futures_pair, 20)
+            assert (isinstance(futures_leverage, float) or isinstance(futures_leverage, int))
+            assert futures_leverage >= 1.0
+
+    def test_ccxt_get_contract_size(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        futures, futures_name = exchange_futures
+        futures_pair = EXCHANGES[futures_name].get(
+            'futures_pair',
+            EXCHANGES[futures_name]['pair']
+        )
+        contract_size = futures.get_contract_size(futures_pair)
+        assert (isinstance(contract_size, float) or isinstance(contract_size, int))
+        assert contract_size >= 0.0
 
     def test_ccxt_load_leverage_tiers(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
         futures, futures_name = exchange_futures
-        if futures and EXCHANGES[futures_name].get('leverage_tiers_public'):
+        if EXCHANGES[futures_name].get('leverage_tiers_public'):
             leverage_tiers = futures.load_leverage_tiers()
             futures_pair = EXCHANGES[futures_name].get(
                 'futures_pair',
@@ -747,7 +406,7 @@ class TestCCXTExchange:
 
     def test_ccxt_dry_run_liquidation_price(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
         futures, futures_name = exchange_futures
-        if futures and EXCHANGES[futures_name].get('leverage_tiers_public'):
+        if EXCHANGES[futures_name].get('leverage_tiers_public'):
 
             futures_pair = EXCHANGES[futures_name].get(
                 'futures_pair',
@@ -780,14 +439,13 @@ class TestCCXTExchange:
 
     def test_ccxt_get_max_pair_stake_amount(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
         futures, futures_name = exchange_futures
-        if futures:
-            futures_pair = EXCHANGES[futures_name].get(
-                'futures_pair',
-                EXCHANGES[futures_name]['pair']
-            )
-            max_stake_amount = futures.get_max_pair_stake_amount(futures_pair, 40000)
-            assert (isinstance(max_stake_amount, float))
-            assert max_stake_amount >= 0.0
+        futures_pair = EXCHANGES[futures_name].get(
+            'futures_pair',
+            EXCHANGES[futures_name]['pair']
+        )
+        max_stake_amount = futures.get_max_pair_stake_amount(futures_pair, 40000)
+        assert (isinstance(max_stake_amount, float))
+        assert max_stake_amount >= 0.0
 
     def test_private_method_presence(self, exchange: EXCHANGE_FIXTURE_TYPE):
         exch, exchangename = exchange

tests/freqai/conftest.py

@@ -97,9 +97,9 @@ def mock_pytorch_mlp_model_training_parameters() -> Dict[str, Any]:
     return {
             "learning_rate": 3e-4,
             "trainer_kwargs": {
-                "max_iters": 1,
+                "n_steps": None,
                 "batch_size": 64,
-                "max_n_eval_batches": 1,
+                "n_epochs": 1,
             },
             "model_kwargs": {
                 "hidden_dim": 32,

tests/optimize/test_backtesting.py

@@ -20,7 +20,7 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history import get_timerange
 from freqtrade.enums import CandleType, ExitType, RunMode
 from freqtrade.exceptions import DependencyException, OperationalException
-from freqtrade.exchange.exchange import timeframe_to_next_date
+from freqtrade.exchange import timeframe_to_next_date, timeframe_to_prev_date
 from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename, get_strategy_run_id
 from freqtrade.optimize.backtesting import Backtesting
 from freqtrade.persistence import LocalTrade, Trade
@@ -1122,10 +1122,10 @@ def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadi
     processed = backtesting.strategy.advise_all_indicators(data)
     min_date, max_date = get_timerange(processed)
 
-    global count
     count = 0
 
     def tmp_confirm_entry(pair, current_time, **kwargs):
+        nonlocal count
         dp = backtesting.strategy.dp
         df, _ = dp.get_analyzed_dataframe(pair, backtesting.strategy.timeframe)
         current_candle = df.iloc[-1].squeeze()
@@ -1135,8 +1135,13 @@ def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadi
         assert candle_date == current_time
         # These asserts don't properly raise as they are nested,
         # therefore we increment count and assert for that.
-        global count
-        count = count + 1
+        df = dp.get_pair_dataframe(pair, backtesting.strategy.timeframe)
+        prior_time = timeframe_to_prev_date(backtesting.strategy.timeframe,
+                                            candle_date - timedelta(seconds=1))
+        assert prior_time == df.iloc[-1].squeeze()['date']
+        assert df.iloc[-1].squeeze()['date'] < current_time
+
+        count += 1
 
     backtesting.strategy.confirm_trade_entry = tmp_confirm_entry
     backtesting.backtest(
@@ -1354,11 +1359,11 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
 
     # Cached data correctly removed amounts
     offset = 1 if tres == 0 else 0
-    removed_candles = len(data[pair]) - offset - backtesting.strategy.startup_candle_count
+    removed_candles = len(data[pair]) - offset
     assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, '5m')[0]) == removed_candles
     assert len(
         backtesting.dataprovider.get_analyzed_dataframe('NXT/BTC', '5m')[0]
-    ) == len(data['NXT/BTC']) - 1 - backtesting.strategy.startup_candle_count
+    ) == len(data['NXT/BTC']) - 1
 
     backtesting.strategy.max_open_trades = 1
     backtesting.config.update({'max_open_trades': 1})

tests/optimize/test_lookahead_analysis.py

@@ -17,6 +17,8 @@ from tests.conftest import EXMS, get_args, log_has_re, patch_exchange
 def lookahead_conf(default_conf_usdt):
     default_conf_usdt['minimum_trade_amount'] = 10
     default_conf_usdt['targeted_trade_amount'] = 20
+    default_conf_usdt['timerange'] = '20220101-20220501'
+
     default_conf_usdt['strategy_path'] = str(
         Path(__file__).parent.parent / "strategy/strats/lookahead_bias")
     default_conf_usdt['strategy'] = 'strategy_test_v3_with_lookahead_bias'
@@ -43,7 +45,9 @@ def test_start_lookahead_analysis(mocker):
         "--pairs",
         "UNITTEST/BTC",
         "--max-open-trades",
-        "1"
+        "1",
+        "--timerange",
+        "20220101-20220201"
     ]
     pargs = get_args(args)
     pargs['config'] = None
@@ -72,6 +76,24 @@ def test_start_lookahead_analysis(mocker):
                        match=r"Targeted trade amount can't be smaller than minimum trade amount.*"):
         start_lookahead_analysis(pargs)
 
+    # Missing timerange
+    args = [
+        "lookahead-analysis",
+        "--strategy",
+        "strategy_test_v3_with_lookahead_bias",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy/strats/lookahead_bias"),
+        "--pairs",
+        "UNITTEST/BTC",
+        "--max-open-trades",
+        "1",
+    ]
+    pargs = get_args(args)
+    pargs['config'] = None
+    with pytest.raises(OperationalException,
+                       match=r"Please set a timerange\..*"):
+        start_lookahead_analysis(pargs)
+
 
 def test_lookahead_helper_invalid_config(lookahead_conf) -> None:
     conf = deepcopy(lookahead_conf)

tests/plugins/test_pairlist.py

@@ -553,7 +553,7 @@ def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, shitcoinmarkets, t
         assert isinstance(whitelist, list)
 
         # Verify length of pairlist matches (used for ShuffleFilter without seed)
-        if type(whitelist_result) is list:
+        if isinstance(whitelist_result, list):
             assert whitelist == whitelist_result
         else:
             len(whitelist) == whitelist_result

tests/rpc/test_rpc_apiserver.py

@@ -1424,12 +1424,12 @@ def test_api_pair_candles(botclient, ohlcv_history):
     assert len(rc.json()['data']) == amount
 
     assert (rc.json()['data'] ==
-            [['2017-11-26 08:50:00', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
+            [['2017-11-26T08:50:00Z', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
               None, 0, 0, 0, 0, 1511686200000, None, None, None, None],
-             ['2017-11-26 08:55:00', 8.88e-05, 8.942e-05, 8.88e-05,
+             ['2017-11-26T08:55:00Z', 8.88e-05, 8.942e-05, 8.88e-05,
                  8.893e-05, 0.05874751, 8.886500000000001e-05, 1, 0, 0, 0, 1511686500000, 8.893e-05,
                  None, None, None],
-             ['2017-11-26 09:00:00', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
+             ['2017-11-26T09:00:00Z', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
                  0.7039405, 8.885e-05, 0, 0, 0, 0, 1511686800000, None, None, None, None]
 
              ])
@@ -1443,13 +1443,13 @@ def test_api_pair_candles(botclient, ohlcv_history):
                     f"{BASE_URI}/pair_candles?limit={amount}&pair=XRP%2FBTC&timeframe={timeframe}")
     assert_response(rc)
     assert (rc.json()['data'] ==
-            [['2017-11-26 08:50:00', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
+            [['2017-11-26T08:50:00Z', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
               None, 0, None, 0, 0, None, 1511686200000, None, None, None, None],
-             ['2017-11-26 08:55:00', 8.88e-05, 8.942e-05, 8.88e-05,
-                 8.893e-05, 0.05874751, 8.886500000000001e-05, 1, 0.0, 0, 0, '2017-11-26 08:55:00',
+             ['2017-11-26T08:55:00Z', 8.88e-05, 8.942e-05, 8.88e-05,
+                 8.893e-05, 0.05874751, 8.886500000000001e-05, 1, 0.0, 0, 0, '2017-11-26T08:55:00Z',
                  1511686500000, 8.893e-05, None, None, None],
-             ['2017-11-26 09:00:00', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
-                 0.7039405, 8.885e-05, 0, 0.0, 0, 0, '2017-11-26 09:00:00', 1511686800000,
+             ['2017-11-26T09:00:00Z', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
+                 0.7039405, 8.885e-05, 0, 0.0, 0, 0, '2017-11-26T09:00:00Z', 1511686800000,
                  None, None, None, None]
              ])
 
@@ -1506,7 +1506,7 @@ def test_api_pair_history(botclient, mocker):
     date_col_idx = [idx for idx, c in enumerate(result['columns']) if c == 'date'][0]
     rsi_col_idx = [idx for idx, c in enumerate(result['columns']) if c == 'rsi'][0]
 
-    assert data[0][date_col_idx] == '2018-01-11 00:00:00'
+    assert data[0][date_col_idx] == '2018-01-11T00:00:00Z'
     assert data[0][rsi_col_idx] is not None
     assert data[0][rsi_col_idx] > 0
     assert lfm.call_count == 1