Strategy analysis example¶
Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data. 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.
Setup¶
```python from pathlib import Path from freqtrade.configuration import Configuration
Customize these according to your needs.¶
Initialize empty configuration object¶
config = Configuration.from_files([])
Optionally, use existing configuration file¶
config = Configuration.from_files(["config.json"])¶
Define some constants¶
config["timeframe"] = "5m"
Name of the strategy class¶
config["strategy"] = "SampleStrategy"
Location of the data¶
data_location = Path(config['user_data_dir'], 'data', 'binance')
Pair to analyze - Only use one pair here¶
pair = "BTC/USDT" ```
```python
Load data using values set above¶
from freqtrade.data.history import load_pair_history
candles = load_pair_history(datadir=data_location, timeframe=config["timeframe"], pair=pair, data_format = "hdf5", )
Confirm success¶
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}") candles.head() ```
Load and run strategy¶
- Rerun each time the strategy file is changed
```python
Load strategy using values set above¶
from freqtrade.resolvers import StrategyResolver from freqtrade.data.dataprovider import DataProvider strategy = StrategyResolver.load_strategy(config) strategy.dp = DataProvider(config, None, None)
Generate buy/sell signals using strategy¶
df = strategy.analyze_ticker(candles, {'pair': pair}) df.tail() ```
Display the trade details¶
- Note that using
data.head()would also work, however most indicators have some "startup" data at the top of the dataframe. - Some possible problems
* Columns with NaN values at the end of the dataframe
* Columns used in
crossed*()functions with completely different units - Comparison with full backtest
* having 200 buy signals as output for one pair from
analyze_ticker()does not necessarily mean that 200 trades will be made during backtesting. * Assuming you use only one condition such as,df['rsi'] < 30as 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.
```python
Report results¶
print(f"Generated {df['buy'].sum()} buy signals") data = df.set_index('date', drop=False) data.tail() ```
Load existing objects into a Jupyter notebook¶
The following cells assume that you have already generated data using the cli.
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.
Load backtest results to pandas dataframe¶
Analyze a trades dataframe (also used below for plotting)
```python from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
if backtest_dir points to a directory, it'll automatically load the last backtest file.¶
backtest_dir = config["user_data_dir"] / "backtest_results"
backtest_dir can also point to a specific file¶
backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"¶
```
```python
You can get the full backtest statistics by using the following command.¶
This contains all information used to generate the backtest result.¶
stats = load_backtest_stats(backtest_dir)
strategy = 'SampleStrategy'
All statistics are available per strategy, so if --strategy-list was used during backtest, this will be reflected here as well.¶
Example usages:¶
print(stats['strategy'][strategy]['results_per_pair'])
Get pairlist used for this backtest¶
print(stats['strategy'][strategy]['pairlist'])
Get market change (average change of all pairs from start to end of the backtest period)¶
print(stats['strategy'][strategy]['market_change'])
Maximum drawdown ()¶
print(stats['strategy'][strategy]['max_drawdown'])
Maximum drawdown start and end¶
print(stats['strategy'][strategy]['drawdown_start']) print(stats['strategy'][strategy]['drawdown_end'])
Get strategy comparison (only relevant if multiple strategies were compared)¶
print(stats['strategy_comparison'])
```
```python
Load backtested trades as dataframe¶
trades = load_backtest_data(backtest_dir)
Show value-counts per pair¶
trades.groupby("pair")["sell_reason"].value_counts() ```
Plotting daily profit / equity line¶
```python
Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)¶
from freqtrade.configuration import Configuration from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats import plotly.express as px import pandas as pd
strategy = 'SampleStrategy'¶
config = Configuration.from_files(["user_data/config.json"])¶
backtest_dir = config["user_data_dir"] / "backtest_results"¶
stats = load_backtest_stats(backtest_dir) strategy_stats = stats['strategy'][strategy]
dates = [] profits = [] for date_profit in strategy_stats['daily_profit']: dates.append(date_profit[0]) profits.append(date_profit[1])
equity = 0 equity_daily = [] for daily_profit in profits: equity_daily.append(equity) equity += float(daily_profit)
df = pd.DataFrame({'dates': dates,'equity_daily': equity_daily})
fig = px.line(df, x="dates", y="equity_daily") fig.show()
```
Load live trading results into a pandas dataframe¶
In case you did already some trading and want to analyze your performance
```python from freqtrade.data.btanalysis import load_trades_from_db
Fetch trades from database¶
trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
Display results¶
trades.groupby("pair")["sell_reason"].value_counts() ```
Analyze the loaded trades for trade parallelism¶
This can be useful to find the best max_open_trades parameter, when used with backtesting in conjunction with --disable-max-market-positions.
analyze_trade_parallelism() returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
```python from freqtrade.data.btanalysis import analyze_trade_parallelism
Analyze the above¶
parallel_trades = analyze_trade_parallelism(trades, '5m')
parallel_trades.plot() ```
Plot results¶
Freqtrade offers interactive plotting capabilities based on plotly.
```python from freqtrade.plot.plotting import generate_candlestick_graph
Limit graph period to keep plotly quick and reactive¶
Filter trades to one pair¶
trades_red = trades.loc[trades['pair'] == pair]
data_red = data['2019-06-01':'2019-06-10']
Generate candlestick graph¶
graph = generate_candlestick_graph(pair=pair, data=data_red, trades=trades_red, indicators1=['sma20', 'ema50', 'ema55'], indicators2=['rsi', 'macd', 'macdsignal', 'macdhist'] )
```
```python
Show graph inline¶
graph.show()¶
Render graph in a seperate window¶
graph.show(renderer="browser")
```
Plot average profit per trade as distribution graph¶
```python import plotly.figure_factory as ff
hist_data = [trades.profit_ratio] group_labels = ['profit_ratio'] # name of the dataset
fig = ff.create_distplot(hist_data, group_labels,bin_size=0.01) fig.show()
```
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.