Merge pull request #1959 from freqtrade/split_btanalysis_load_trades
Split btanalysis load trades
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Matthias
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31a2aac627
@ -221,24 +221,8 @@ strategies, your configuration, and the crypto-currency you have set up.
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### Further backtest-result analysis
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To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
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You can then load the trades to perform further analysis.
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You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
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A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
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Freqtrade provides an easy to load the backtest results, which is `load_backtest_data` - and takes a path to the backtest-results file.
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``` python
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from freqtrade.data.btanalysis import load_backtest_data
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df = load_backtest_data("user_data/backtest-result.json")
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# Show value-counts per pair
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df.groupby("pair")["sell_reason"].value_counts()
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```
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This will allow you to drill deeper into your backtest results, and perform analysis which would make the regular backtest-output unreadable.
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If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a PR so the community can benefit from it.
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## Backtesting multiple strategies
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42
docs/data-analysis.md
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42
docs/data-analysis.md
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@ -0,0 +1,42 @@
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# Analyzing bot data
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After performing backtests, or after running the bot for some time, it will be interesting to analyze the results your bot generated.
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A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
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The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.
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## Backtesting
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To analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
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You can then load the trades to perform further analysis.
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Freqtrade provides the `load_backtest_data()` helper function to easily load the backtest results, which takes the path to the the backtest-results file as parameter.
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``` python
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from freqtrade.data.btanalysis import load_backtest_data
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df = load_backtest_data("user_data/backtest-result.json")
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# Show value-counts per pair
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df.groupby("pair")["sell_reason"].value_counts()
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```
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This will allow you to drill deeper into your backtest results, and perform analysis which otherwise would make the regular backtest-output very difficult to digest due to information overload.
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If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a Pull Request so the community can benefit from it.
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## Live data
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To analyze the trades your bot generated, you can load them to a DataFrame as follows:
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``` python
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from freqtrade.data.btanalysis import load_trades_from_db
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df = load_trades_from_db("sqlite:///tradesv3.sqlite")
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df.groupby("pair")["sell_reason"].value_counts()
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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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@ -58,7 +58,7 @@ Timerange doesn't work with live data.
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To plot trades stored in a database use `--db-url` argument:
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``` bash
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python3 scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH
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python3 scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
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```
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To plot trades from a backtesting result, use `--export-filename <filename>`
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@ -516,3 +516,11 @@ class Arguments(object):
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default=750,
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type=int,
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)
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parser.add_argument(
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'--trade-source',
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help='Specify the source for trades (Can be DB or file (backtest file)) '
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'Default: %(default)s',
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dest='trade_source',
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default="file",
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choices=["DB", "file"]
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)
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@ -358,7 +358,8 @@ class Configuration(object):
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self._args_to_config(config, argname='plot_limit',
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logstring='Limiting plot to: {}')
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self._args_to_config(config, argname='trade_source',
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logstring='Using trades from: {}')
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return config
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def _validate_config_schema(self, conf: Dict[str, Any]) -> Dict[str, Any]:
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@ -73,37 +73,30 @@ def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int
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return df_final[df_final['pair'] > max_open_trades]
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def load_trades(db_url: str = None, exportfilename: str = None) -> pd.DataFrame:
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def load_trades_from_db(db_url: str) -> pd.DataFrame:
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"""
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Load trades, either from a DB (using dburl) or via a backtest export file.
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Load trades from a DB (using dburl)
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:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
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:param exportfilename: Path to a file exported from backtesting
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:return: Dataframe containing Trades
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"""
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timeZone = pytz.UTC
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trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
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persistence.init(db_url, clean_open_orders=False)
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columns = ["pair", "profit", "open_time", "close_time",
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"open_rate", "close_rate", "duration", "sell_reason",
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"max_rate", "min_rate"]
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if db_url:
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persistence.init(db_url, clean_open_orders=False)
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columns = ["pair", "profit", "open_time", "close_time",
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"open_rate", "close_rate", "duration"]
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for x in Trade.query.all():
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logger.info("date: {}".format(x.open_date))
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trades = pd.DataFrame([(t.pair, t.calc_profit(),
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t.open_date.replace(tzinfo=timeZone),
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t.close_date.replace(tzinfo=timeZone) if t.close_date else None,
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t.open_rate, t.close_rate,
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t.close_date.timestamp() - t.open_date.timestamp()
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if t.close_date else None)
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for t in Trade.query.all()],
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columns=columns)
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elif exportfilename:
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trades = load_backtest_data(Path(exportfilename))
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trades = pd.DataFrame([(t.pair, t.calc_profit(),
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t.open_date.replace(tzinfo=pytz.UTC),
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t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
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t.open_rate, t.close_rate,
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t.close_date.timestamp() - t.open_date.timestamp()
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if t.close_date else None,
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t.sell_reason,
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t.max_rate,
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t.min_rate,
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)
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for t in Trade.query.all()],
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columns=columns)
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return trades
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@ -81,6 +81,8 @@ def plot_trades(fig, trades: pd.DataFrame):
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)
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fig.append_trace(trade_buys, 1, 1)
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fig.append_trace(trade_sells, 1, 1)
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else:
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logger.warning("No trades found.")
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return fig
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@ -7,7 +7,7 @@ from pandas import DataFrame, to_datetime
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from freqtrade.arguments import TimeRange
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from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
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extract_trades_of_period,
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load_backtest_data, load_trades)
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load_backtest_data, load_trades_from_db)
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from freqtrade.data.history import load_pair_history, make_testdata_path
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from freqtrade.tests.test_persistence import create_mock_trades
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@ -28,14 +28,6 @@ def test_load_backtest_data():
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load_backtest_data(str("filename") + "nofile")
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def test_load_trades_file(default_conf, fee, mocker):
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# Real testing of load_backtest_data is done in test_load_backtest_data
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lbt = mocker.patch("freqtrade.data.btanalysis.load_backtest_data", MagicMock())
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filename = make_testdata_path(None) / "backtest-result_test.json"
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load_trades(db_url=None, exportfilename=filename)
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assert lbt.call_count == 1
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@pytest.mark.usefixtures("init_persistence")
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def test_load_trades_db(default_conf, fee, mocker):
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@ -43,7 +35,7 @@ def test_load_trades_db(default_conf, fee, mocker):
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# remove init so it does not init again
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init_mock = mocker.patch('freqtrade.persistence.init', MagicMock())
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trades = load_trades(db_url=default_conf['db_url'], exportfilename=None)
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trades = load_trades_from_db(db_url=default_conf['db_url'])
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assert init_mock.call_count == 1
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assert len(trades) == 3
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assert isinstance(trades, DataFrame)
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@ -67,11 +67,12 @@ def test_generate_row(default_conf, caplog):
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assert log_has_re(r'Indicator "no_indicator" ignored\..*', caplog.record_tuples)
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def test_plot_trades():
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def test_plot_trades(caplog):
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fig1 = generage_empty_figure()
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# nothing happens when no trades are available
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fig = plot_trades(fig1, None)
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assert fig == fig1
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assert log_has("No trades found.", caplog.record_tuples)
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pair = "ADA/BTC"
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filename = history.make_testdata_path(None) / "backtest-result_test.json"
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trades = load_backtest_data(filename)
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@ -17,6 +17,7 @@ nav:
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- Plotting: plotting.md
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- Deprecated features: deprecated.md
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- FAQ: faq.md
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- Data Analysis: data-analysis.md
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- SQL Cheatsheet: sql_cheatsheet.md
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- Sandbox testing: sandbox-testing.md
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- Contributors guide: developer.md
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@ -33,10 +33,10 @@ import pandas as pd
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from freqtrade.arguments import Arguments
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from freqtrade.data import history
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from freqtrade.data.btanalysis import load_trades, extract_trades_of_period
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from freqtrade.data.btanalysis import (extract_trades_of_period,
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load_backtest_data, load_trades_from_db)
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from freqtrade.optimize import setup_configuration
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from freqtrade.plot.plotting import (generate_graph,
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generate_plot_file)
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from freqtrade.plot.plotting import generate_graph, generate_plot_file
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from freqtrade.resolvers import ExchangeResolver, StrategyResolver
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from freqtrade.state import RunMode
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@ -97,9 +97,11 @@ def analyse_and_plot_pairs(config: Dict[str, Any]):
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tickers = {}
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tickers[pair] = data
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dataframe = generate_dataframe(strategy, tickers, pair)
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if config["trade_source"] == "DB":
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trades = load_trades_from_db(config["db_url"])
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elif config["trade_source"] == "file":
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trades = load_backtest_data(Path(config["exportfilename"]))
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trades = load_trades(db_url=config["db_url"],
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exportfilename=config["exportfilename"])
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trades = trades.loc[trades['pair'] == pair]
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trades = extract_trades_of_period(dataframe, trades)
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