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159 lines
4.8 KiB
Markdown
159 lines
4.8 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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## Setup
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```python
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from pathlib import Path
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# Customize these according to your needs.
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# Define some constants
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timeframe = "5m"
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# Name of the strategy class
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strategy_name = 'SampleStrategy'
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# Path to user data
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user_data_dir = Path('user_data')
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# Location of the strategy
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strategy_location = user_data_dir / 'strategies'
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# Location of the data
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data_location = Path(user_data_dir, 'data', 'binance')
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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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candles = load_pair_history(datadir=data_location,
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timeframe=timeframe,
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pair=pair)
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# Confirm success
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print("Loaded " + str(len(candles)) + f" 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.resolvers import StrategyResolver
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strategy = StrategyResolver({'strategy': strategy_name,
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'user_data_dir': user_data_dir,
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'strategy_path': strategy_location}).strategy
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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['buy'].sum()} buy 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
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# Load backtest results
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trades = load_backtest_data(user_data_dir / "backtest_results/backtest-result.json")
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# Show value-counts per pair
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trades.groupby("pair")["sell_reason"].value_counts()
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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")["sell_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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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,
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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 seperate window
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graph.show(renderer="browser")
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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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