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282 lines
8.1 KiB
Markdown
282 lines
8.1 KiB
Markdown
# Strategy analysis example
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Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
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The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.
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Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details.
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## Setup
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### Change Working directory to repository root
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```python
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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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try:
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os.chdir(project_root)
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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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### Configure Freqtrade environment
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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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config = Configuration.from_files([])
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# Optionally (recommended), use existing configuration file
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# config = Configuration.from_files(["user_data/config.json"])
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# Define some constants
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config["timeframe"] = "5m"
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# Name of the strategy class
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config["strategy"] = "SampleStrategy"
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# Location of the data
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data_location = config["datadir"]
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# Pair to analyze - Only use one pair here
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pair = "BTC/USDT"
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```
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```python
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# Load data using values set above
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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(
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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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candles.head()
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```
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## Load and run strategy
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* Rerun each time the strategy file is changed
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```python
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# Load strategy using values set above
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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.tail()
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```
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### Display the trade details
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* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
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* Some possible problems
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* Columns with NaN values at the end of the dataframe
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* Columns used in `crossed*()` functions with completely different units
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* Comparison with full backtest
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* having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
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* Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
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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.tail()
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```
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## Load existing objects into a Jupyter notebook
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The following cells assume that you have already generated data using the cli.
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They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
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### Load backtest results to pandas dataframe
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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 = (
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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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```python
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# You can get the full backtest statistics by using the following command.
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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,
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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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# Get pairlist used for this backtest
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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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# Maximum 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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# Get strategy comparison (only relevant if multiple strategies were compared)
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print(stats["strategy_comparison"])
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```
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```python
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# Load backtested trades as dataframe
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trades = load_backtest_data(backtest_dir)
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# Show value-counts per pair
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trades.groupby("pair")["exit_reason"].value_counts()
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```
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## Plotting daily profit / equity line
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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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# 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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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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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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# Display results
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trades.groupby("pair")["exit_reason"].value_counts()
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```
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## Analyze the loaded trades for trade parallelism
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This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.
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`analyze_trade_parallelism()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
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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.plot()
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```
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## Plot results
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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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# 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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data_red = data["2019-06-01":"2019-06-10"]
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# Generate candlestick graph
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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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```python
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# Show graph inline
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# graph.show()
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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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```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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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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