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# Advanced Orderflow # Advanced Orderflow
This page explains some advanced tasks and configuration options that can be performed to use orderflow data by downloading public trade data. This guide walks you through utilizing public trade data for advanced orderflow analysis in Freqtrade.
## Quickstart ## Getting Started
enable using public trades in `config.json` 1. **Enable Public Trades:** in your `config.json` file, set the `use_public_trades` option to true under the `exchange` section.
``` ```json
"exchange": { "exchange": {
... ...
"use_public_trades": true, "use_public_trades": true,
} }
``` ```
set orderflow processing configuration in `config.json`: 2. **Configure Orderflow Processing:** Define your desired settings for orderflow processing within the orderflow section of config.json. Here, you can adjust factors like:
```
- `scale`: This controls the price bin size for the footprint chart.
- `stacked_imbalance_range`: Defines the minimum consecutive imbalanced price levels required for consideration.
- `imbalance_volume`: Filters out imbalances with volume below this threshold.
- `imbalance_ratio`: Filters out imbalances with a ratio (difference between ask and bid volume) lower than this value.
```json
"orderflow": { "orderflow": {
"scale": 0.5, "scale": 0.5,
"stacked_imbalance_range": 3, # needs at least this amount of imblance next to each other "stacked_imbalance_range": 3, # needs at least this amount of imblance next to each other
@@ -22,57 +28,59 @@ set orderflow processing configuration in `config.json`:
}, },
``` ```
## Downloading data for backtesting ## Downloading Trade Data for Backtesting
- use `--dl-trades` to fetch trades for timerange To download historical trade data for backtesting, use the --dl-trades flag with the freqtrade download-data command.
For example ```bash
``` bash
freqtrade download-data -p BTC/USDT:USDT --timerange 20230101- --trading-mode futures --timeframes 5m --dl-trades freqtrade download-data -p BTC/USDT:USDT --timerange 20230101- --trading-mode futures --timeframes 5m --dl-trades
``` ```
## Accessing orderflow data ## Accessing Orderflow Data
Several new columns are available when activated. Once activated, several new columns become available in your dataframe:
``` python ``` python
dataframe['trades'] # every single trade dataframe['trades'] # Contains information about each individual trade.
dataframe['orderflow'] # footprint chart: see below dataframe['orderflow'] # Represents a footprint chart dataframe (see below)
dataframe['bid'] # bid sum dataframe['bid'] # Total bid volume
dataframe['ask'] # ask sum dataframe['ask'] # Total ask volume
dataframe['delta'] # ask - bid dataframe['delta'] # Difference between ask and bid volume.
dataframe['min_delta'] # minimum delta reached within candle dataframe['min_delta'] # Minimum delta within the candle
dataframe['max_delta'] # maximum delta reached within candle dataframe['max_delta'] # Maximum delta within the candle
dataframe['total_trades'] # amount of trades dataframe['total_trades'] # Total number of trades
dataframe['stacked_imbalances_bid'] # price level stacked imbalance bid occurred dataframe['stacked_imbalances_bid'] # Price level of stacked bid imbalance
dataframe['stacked_imbalances_ask'] # price level stacked imbalance ask occurred dataframe['stacked_imbalances_ask'] # Price level of stacked ask imbalance
``` ```
These can be accessed like this: You can access these columns in your strategy code for further analysis. Here's an example:
``` python ``` python
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# calculating cumulative delta # Calculating cumulative delta
dataframe['cum_delta'] = cumulative_delta(dataframe['delta']) dataframe['cum_delta'] = cumulative_delta(dataframe['delta'])
# Accessing total trades
total_trades = dataframe['total_trades']
...
def cumulative_delta(delta: Series): def cumulative_delta(delta: Series):
cumdelta = delta.cumsum() cumdelta = delta.cumsum()
return cumdelta return cumdelta
``` ```
### dataframe['orderflow'] ### Footprint chart (dataframe['orderflow'])
This includes a dataframe that represents a Footprint chart of the Bid vs Ask type. Footprint charts are a type of candlestick chart that provides additional information, such as trade volume and order flow, in addition to price. This column provides a detailed breakdown of buy and sell orders at different price levels, offering valuable insights into order flow dynamics. The scale parameter in your configuration determines the price bin size for this representation
The scale of the price is set by `orderflow.scale` (see above) and thus binned per price level.
Following columns are available: The `orderflow` dataframe includes columns like:
```python
orderflow_df['bid_amount'] # how much bids were traded - `bid_amount`: Total volume bought at each price level.
orderflow_df['ask_amount'] # how much asks were traded - `ask_amount`: Total volume sold at each price level.
orderflow_df['bid'] # how many bids trades - `bid`: Number of buy orders at each price level.
orderflow_df['ask'] # how many asks trades - `ask`: Number of sell orders at each price level.
orderflow_df['delta'] # ask amount - bid amount - `delta`: Difference between ask and bid volume at each price level.
orderflow_df['total_volume'] # ask amount + bid amount - `total_volume`: Total volume (ask amount + bid amount) at each price level.
orderflow_df['total_trades'] # ask + bid trades - `total_trades`: Total number of trades (ask + bid) at each price level.
```
By leveraging these features, you can gain valuable insights into market sentiment and potential trading opportunities based on order flow analysis.