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https://github.com/freqtrade/freqtrade.git
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Merge pull request #10553 from freqtrade/dependabot/pip/develop/ruff-0.6.1
chore(deps-dev): bump ruff from 0.5.7 to 0.6.1
This commit is contained in:
Matthias
@@ -13,19 +13,22 @@ Please follow the [documentation](https://www.freqtrade.io/en/stable/data-downlo
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import os
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from pathlib import Path
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# Change directory
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# Modify this cell to insure that the output shows the correct path.
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# Define all paths relative to the project root shown in the cell output
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project_root = "somedir/freqtrade"
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i=0
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i = 0
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try:
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os.chdir(project_root)
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assert Path('LICENSE').is_file()
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except:
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while i<4 and (not Path('LICENSE').is_file()):
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os.chdir(Path(Path.cwd(), '../'))
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i+=1
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project_root = Path.cwd()
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if not Path("LICENSE").is_file():
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i = 0
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while i < 4 and (not Path("LICENSE").is_file()):
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os.chdir(Path(Path.cwd(), "../"))
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i += 1
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project_root = Path.cwd()
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except FileNotFoundError:
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print("Please define the project root relative to the current directory")
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print(Path.cwd())
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```
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@@ -35,6 +38,7 @@ print(Path.cwd())
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```python
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from freqtrade.configuration import Configuration
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# Customize these according to your needs.
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# Initialize empty configuration object
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@@ -58,12 +62,14 @@ pair = "BTC/USDT"
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from freqtrade.data.history import load_pair_history
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from freqtrade.enums import CandleType
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candles = load_pair_history(datadir=data_location,
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timeframe=config["timeframe"],
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pair=pair,
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data_format = "json", # Make sure to update this to your data
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candle_type=CandleType.SPOT,
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)
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candles = load_pair_history(
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datadir=data_location,
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timeframe=config["timeframe"],
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pair=pair,
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data_format="json", # Make sure to update this to your data
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candle_type=CandleType.SPOT,
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)
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# Confirm success
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print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
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@@ -76,14 +82,16 @@ candles.head()
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```python
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# Load strategy using values set above
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from freqtrade.resolvers import StrategyResolver
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.resolvers import StrategyResolver
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strategy = StrategyResolver.load_strategy(config)
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strategy.dp = DataProvider(config, None, None)
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strategy.ft_bot_start()
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# Generate buy/sell signals using strategy
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df = strategy.analyze_ticker(candles, {'pair': pair})
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df = strategy.analyze_ticker(candles, {"pair": pair})
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df.tail()
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```
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@@ -102,7 +110,7 @@ df.tail()
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```python
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# Report results
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print(f"Generated {df['enter_long'].sum()} entry signals")
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data = df.set_index('date', drop=False)
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data = df.set_index("date", drop=False)
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data.tail()
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```
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@@ -119,10 +127,13 @@ Analyze a trades dataframe (also used below for plotting)
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```python
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from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
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# if backtest_dir points to a directory, it'll automatically load the last backtest file.
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backtest_dir = config["user_data_dir"] / "backtest_results"
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# backtest_dir can also point to a specific file
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# backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
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# backtest_dir can also point to a specific file
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# backtest_dir = (
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# config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
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# )
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```
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@@ -131,24 +142,24 @@ backtest_dir = config["user_data_dir"] / "backtest_results"
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# This contains all information used to generate the backtest result.
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stats = load_backtest_stats(backtest_dir)
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strategy = 'SampleStrategy'
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# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.
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strategy = "SampleStrategy"
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# All statistics are available per strategy, so if `--strategy-list` was used during backtest,
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# this will be reflected here as well.
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# Example usages:
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print(stats['strategy'][strategy]['results_per_pair'])
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print(stats["strategy"][strategy]["results_per_pair"])
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# Get pairlist used for this backtest
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print(stats['strategy'][strategy]['pairlist'])
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print(stats["strategy"][strategy]["pairlist"])
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# Get market change (average change of all pairs from start to end of the backtest period)
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print(stats['strategy'][strategy]['market_change'])
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print(stats["strategy"][strategy]["market_change"])
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# Maximum drawdown ()
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print(stats['strategy'][strategy]['max_drawdown'])
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print(stats["strategy"][strategy]["max_drawdown"])
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# Maximum drawdown start and end
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print(stats['strategy'][strategy]['drawdown_start'])
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print(stats['strategy'][strategy]['drawdown_end'])
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print(stats["strategy"][strategy]["drawdown_start"])
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print(stats["strategy"][strategy]["drawdown_end"])
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# Get strategy comparison (only relevant if multiple strategies were compared)
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print(stats['strategy_comparison'])
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print(stats["strategy_comparison"])
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```
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@@ -166,24 +177,25 @@ trades.groupby("pair")["exit_reason"].value_counts()
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```python
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# Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)
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import pandas as pd
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import plotly.express as px
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from freqtrade.configuration import Configuration
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from freqtrade.data.btanalysis import load_backtest_stats
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import plotly.express as px
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import pandas as pd
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# strategy = 'SampleStrategy'
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# config = Configuration.from_files(["user_data/config.json"])
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# backtest_dir = config["user_data_dir"] / "backtest_results"
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stats = load_backtest_stats(backtest_dir)
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strategy_stats = stats['strategy'][strategy]
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strategy_stats = stats["strategy"][strategy]
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df = pd.DataFrame(columns=['dates','equity'], data=strategy_stats['daily_profit'])
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df['equity_daily'] = df['equity'].cumsum()
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df = pd.DataFrame(columns=["dates", "equity"], data=strategy_stats["daily_profit"])
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df["equity_daily"] = df["equity"].cumsum()
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fig = px.line(df, x="dates", y="equity_daily")
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fig.show()
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```
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### Load live trading results into a pandas dataframe
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@@ -194,6 +206,7 @@ In case you did already some trading and want to analyze your performance
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```python
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from freqtrade.data.btanalysis import load_trades_from_db
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# Fetch trades from database
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trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
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@@ -210,8 +223,9 @@ This can be useful to find the best `max_open_trades` parameter, when used with
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```python
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from freqtrade.data.btanalysis import analyze_trade_parallelism
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# Analyze the above
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parallel_trades = analyze_trade_parallelism(trades, '5m')
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parallel_trades = analyze_trade_parallelism(trades, "5m")
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parallel_trades.plot()
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```
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@@ -222,23 +236,23 @@ Freqtrade offers interactive plotting capabilities based on plotly.
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```python
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from freqtrade.plot.plotting import generate_candlestick_graph
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from freqtrade.plot.plotting import generate_candlestick_graph
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# Limit graph period to keep plotly quick and reactive
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# Filter trades to one pair
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trades_red = trades.loc[trades['pair'] == pair]
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trades_red = trades.loc[trades["pair"] == pair]
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data_red = data['2019-06-01':'2019-06-10']
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data_red = data["2019-06-01":"2019-06-10"]
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# Generate candlestick graph
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graph = generate_candlestick_graph(pair=pair,
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data=data_red,
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trades=trades_red,
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indicators1=['sma20', 'ema50', 'ema55'],
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indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
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)
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graph = generate_candlestick_graph(
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pair=pair,
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data=data_red,
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trades=trades_red,
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indicators1=["sma20", "ema50", "ema55"],
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indicators2=["rsi", "macd", "macdsignal", "macdhist"],
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)
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```
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@@ -248,7 +262,6 @@ graph = generate_candlestick_graph(pair=pair,
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# Render graph in a separate window
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graph.show(renderer="browser")
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```
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## Plot average profit per trade as distribution graph
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@@ -257,12 +270,12 @@ graph.show(renderer="browser")
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```python
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import plotly.figure_factory as ff
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hist_data = [trades.profit_ratio]
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group_labels = ['profit_ratio'] # name of the dataset
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group_labels = ["profit_ratio"] # name of the dataset
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fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)
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fig.show()
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```
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Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
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