diff --git a/docs/backtesting.md b/docs/backtesting.md index f6c9dd4d1..e155dce88 100644 --- a/docs/backtesting.md +++ b/docs/backtesting.md @@ -245,6 +245,26 @@ On the other hand, if you set a too high `minimal_roi` like `"0": 0.55` profit. Hence, keep in mind that your performance is a mix of your strategies, your configuration, and the crypto-currency you have set up. +### Further backtest-result analysis + +To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file). +You can then load the trades to perform further analysis. + +A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data. + +Freqtrade provides an easy to load the backtest results, which is `load_backtest_data` - and takes a path to the backtest-results file. + +``` python +from freqtrade.data.btanalysis import load_backtest_data +df = load_backtest_data("user_data/backtest-result.json") + +# Show value-counts per pair +df.groupby("pair")["sell_reason"].value_counts() + +``` + +This will allow you to drill deeper into your backtest results, and perform analysis which would make the regular backtest-output unreadable. + ## Backtesting multiple strategies To backtest multiple strategies, a list of Strategies can be provided. diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py new file mode 100644 index 000000000..6fce4361b --- /dev/null +++ b/freqtrade/data/btanalysis.py @@ -0,0 +1,67 @@ +""" +Helpers when analyzing backtest data +""" +from pathlib import Path + +import numpy as np +import pandas as pd + +from freqtrade.misc import json_load + +# must align with columns in backtest.py +BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration", + "open_rate", "close_rate", "open_at_end", "sell_reason"] + + +def load_backtest_data(filename) -> pd.DataFrame: + """ + Load backtest data file. + :param filename: pathlib.Path object, or string pointing to the file. + :return a dataframe with the analysis results + """ + if isinstance(filename, str): + filename = Path(filename) + + if not filename.is_file(): + raise ValueError("File {filename} does not exist.") + + with filename.open() as file: + data = json_load(file) + + df = pd.DataFrame(data, columns=BT_DATA_COLUMNS) + + df['open_time'] = pd.to_datetime(df['open_time'], + unit='s', + utc=True, + infer_datetime_format=True + ) + df['close_time'] = pd.to_datetime(df['close_time'], + unit='s', + utc=True, + infer_datetime_format=True + ) + df['profitabs'] = df['close_rate'] - df['open_rate'] + df = df.sort_values("open_time").reset_index(drop=True) + return df + + +def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame: + """ + Find overlapping trades by expanding each trade once per period it was open + and then counting overlaps + :param results: Results Dataframe - can be loaded + :param freq: Frequency used for the backtest + :param max_open_trades: parameter max_open_trades used during backtest run + :return: dataframe with open-counts per time-period in freq + """ + dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq)) + for row in results[['open_time', 'close_time']].iterrows()] + deltas = [len(x) for x in dates] + dates = pd.Series(pd.concat(dates).values, name='date') + df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns) + + df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"}) + df2 = pd.concat([dates, df2], axis=1) + df2 = df2.set_index('date') + df_final = df2.resample(freq)[['pair']].count() + return df_final[df_final['pair'] > max_open_trades] diff --git a/freqtrade/tests/data/test_btanalysis.py b/freqtrade/tests/data/test_btanalysis.py new file mode 100644 index 000000000..dd7cbe0d9 --- /dev/null +++ b/freqtrade/tests/data/test_btanalysis.py @@ -0,0 +1,21 @@ +import pytest +from pandas import DataFrame + +from freqtrade.data.btanalysis import BT_DATA_COLUMNS, load_backtest_data +from freqtrade.data.history import make_testdata_path + + +def test_load_backtest_data(): + + filename = make_testdata_path(None) / "backtest-result_test.json" + bt_data = load_backtest_data(filename) + assert isinstance(bt_data, DataFrame) + assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profitabs"] + assert len(bt_data) == 179 + + # Test loading from string (must yield same result) + bt_data2 = load_backtest_data(str(filename)) + assert bt_data.equals(bt_data2) + + with pytest.raises(ValueError, match=r"File .* does not exist\."): + load_backtest_data(str("filename") + "nofile") diff --git a/freqtrade/tests/optimize/test_backtesting.py b/freqtrade/tests/optimize/test_backtesting.py index 1d5fb1384..40754cfbc 100644 --- a/freqtrade/tests/optimize/test_backtesting.py +++ b/freqtrade/tests/optimize/test_backtesting.py @@ -14,6 +14,7 @@ from arrow import Arrow from freqtrade import DependencyException, constants from freqtrade.arguments import Arguments, TimeRange from freqtrade.data import history +from freqtrade.data.btanalysis import evaluate_result_multi from freqtrade.data.converter import parse_ticker_dataframe from freqtrade.optimize import get_timeframe from freqtrade.optimize.backtesting import (Backtesting, setup_configuration, @@ -684,21 +685,6 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker): def test_backtest_multi_pair(default_conf, fee, mocker): - def evaluate_result_multi(results, freq, max_open_trades): - # Find overlapping trades by expanding each trade once per period - # and then counting overlaps - dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq)) - for row in results[['open_time', 'close_time']].iterrows()] - deltas = [len(x) for x in dates] - dates = pd.Series(pd.concat(dates).values, name='date') - df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns) - - df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"}) - df2 = pd.concat([dates, df2], axis=1) - df2 = df2.set_index('date') - df_final = df2.resample(freq)[['pair']].count() - return df_final[df_final['pair'] > max_open_trades] - def _trend_alternate_hold(dataframe=None, metadata=None): """ Buy every 8th candle - sell every other 8th -2 (hold on to pairs a bit) diff --git a/freqtrade/tests/testdata/backtest-result_test.json b/freqtrade/tests/testdata/backtest-result_test.json new file mode 100644 index 000000000..8701451dc --- /dev/null +++ b/freqtrade/tests/testdata/backtest-result_test.json @@ -0,0 +1 @@ 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/BTC",-0.0,1516423800.0,1516461300.0,2878,625,0.00270505,0.002718609147869674,false,"roi"],["XMR/BTC",-0.0,1516423800.0,1516431600.0,2878,130,0.03000002,0.030150396040100245,false,"roi"],["ADA/BTC",-0.0,1516438800.0,1516441200.0,2928,40,5.46e-05,5.4873684210526304e-05,false,"roi"],["XMR/BTC",-0.10448878,1516472700.0,1516852200.0,3041,6325,0.03082222,0.027739998000000002,false,"stop_loss"],["ETH/BTC",-0.0,1516487100.0,1516490100.0,3089,50,0.08969999,0.09014961401002504,false,"roi"],["LTC/BTC",0.0,1516503000.0,1516545000.0,3142,700,0.01632501,0.01640683962406015,false,"roi"],["DASH/BTC",-0.0,1516530000.0,1516532400.0,3232,40,0.070538,0.07089157393483708,false,"roi"],["ADA/BTC",-0.0,1516549800.0,1516560300.0,3298,175,5.301e-05,5.3275714285714276e-05,false,"roi"],["XLM/BTC",0.0,1516551600.0,1516554000.0,3304,40,3.955e-05,3.9748245614035085e-05,false,"roi"],["ETC/BTC",0.00997506,1516569300.0,1516571100.0,3363,30,0.00258505,0.002623922932330827,false,"roi"],["XLM/BTC",-0.0,1516569300.0,1516571700.0,3363,40,3.903e-05,3.922563909774435e-05,false,"roi"],["ADA/BTC",-0.0,1516581300.0,1516617300.0,3403,600,5.236e-05,5.262245614035087e-05,false,"roi"],["POWR/BTC",0.0,1516584600.0,1516587000.0,3414,40,9.028e-05,9.073253132832079e-05,false,"roi"],["ETC/BTC",-0.0,1516623900.0,1516631700.0,3545,130,0.002687,0.002700468671679198,false,"roi"],["XLM/BTC",-0.0,1516626900.0,1516629300.0,3555,40,4.168e-05,4.1888922305764405e-05,false,"roi"],["POWR/BTC",0.00997506,1516629600.0,1516631400.0,3564,30,8.821e-05,8.953646616541353e-05,false,"roi"],["ADA/BTC",-0.0,1516636500.0,1516639200.0,3587,45,5.172e-05,5.1979248120300745e-05,false,"roi"],["NXT/BTC",0.01995012,1516637100.0,1516638300.0,3589,20,3.026e-05,3.101839598997494e-05,false,"roi"],["DASH/BTC",0.0,1516650600.0,1516666200.0,3634,260,0.07064,0.07099408521303258,false,"roi"],["LTC/BTC",0.0,1516656300.0,1516658700.0,3653,40,0.01644483,0.01652726022556391,false,"roi"],["XLM/BTC",0.00997506,1516665900.0,1516667700.0,3685,30,4.331e-05,4.3961278195488714e-05,false,"roi"],["NXT/BTC",0.01995012,1516672200.0,1516673700.0,3706,25,3.2e-05,3.2802005012531326e-05,false,"roi"],["ETH/BTC",0.0,1516681500.0,1516684500.0,3737,50,0.09167706,0.09213659413533835,false,"roi"],["DASH/BTC",0.0,1516692900.0,1516698000.0,3775,85,0.0692498,0.06959691679197995,false,"roi"],["NXT/BTC",0.0,1516704600.0,1516712700.0,3814,135,3.182e-05,3.197949874686716e-05,false,"roi"],["ZEC/BTC",-0.0,1516705500.0,1516723500.0,3817,300,0.04088,0.04108491228070175,false,"roi"],["ADA/BTC",-0.0,1516719300.0,1516721700.0,3863,40,5.15e-05,5.175814536340851e-05,false,"roi"],["ETH/BTC",0.0,1516725300.0,1516752300.0,3883,450,0.09071698,0.09117170170426064,false,"roi"],["NXT/BTC",-0.0,1516728300.0,1516733100.0,3893,80,3.128e-05,3.1436791979949865e-05,false,"roi"],["POWR/BTC",-0.0,1516738500.0,1516744800.0,3927,105,9.555e-05,9.602894736842104e-05,false,"roi"],["ZEC/BTC",-0.0,1516746600.0,1516749000.0,3954,40,0.04080001,0.041004521328320796,false,"roi"],["ADA/BTC",-0.0,1516751400.0,1516764900.0,3970,225,5.163e-05,5.1888796992481196e-05,false,"roi"],["ZEC/BTC",0.0,1516753200.0,1516758600.0,3976,90,0.04040781,0.04061035541353383,false,"roi"],["ADA/BTC",-0.0,1516776300.0,1516778700.0,4053,40,5.132e-05,5.157724310776942e-05,false,"roi"],["ADA/BTC",0.03990025,1516803300.0,1516803900.0,4143,10,5.198e-05,5.432496240601503e-05,false,"roi"],["NXT/BTC",-0.0,1516805400.0,1516811700.0,4150,105,3.054e-05,3.069308270676692e-05,false,"roi"],["POWR/BTC",0.0,1516806600.0,1516810500.0,4154,65,9.263e-05,9.309431077694235e-05,false,"roi"],["ADA/BTC",-0.0,1516833600.0,1516836300.0,4244,45,5.514e-05,5.5416390977443596e-05,false,"roi"],["XLM/BTC",0.0,1516841400.0,1516843800.0,4270,40,4.921e-05,4.9456666666666664e-05,false,"roi"],["ETC/BTC",0.0,1516868100.0,1516882500.0,4359,240,0.0026,0.002613032581453634,false,"roi"],["XMR/BTC",-0.0,1516875900.0,1516896900.0,4385,350,0.02799871,0.028139054411027563,false,"roi"],["ZEC/BTC",-0.0,1516878000.0,1516880700.0,4392,45,0.04078902,0.0409934762406015,false,"roi"],["NXT/BTC",-0.0,1516885500.0,1516887900.0,4417,40,2.89e-05,2.904486215538847e-05,false,"roi"],["ZEC/BTC",-0.0,1516886400.0,1516889100.0,4420,45,0.041103,0.041309030075187964,false,"roi"],["XLM/BTC",0.00997506,1516895100.0,1516896900.0,4449,30,5.428e-05,5.5096240601503756e-05,false,"roi"],["XLM/BTC",-0.0,1516902300.0,1516922100.0,4473,330,5.414e-05,5.441137844611528e-05,false,"roi"],["ZEC/BTC",-0.0,1516914900.0,1516917300.0,4515,40,0.04140777,0.0416153277443609,false,"roi"],["ETC/BTC",0.0,1516932300.0,1516934700.0,4573,40,0.00254309,0.002555837318295739,false,"roi"],["ADA/BTC",-0.0,1516935300.0,1516979400.0,4583,735,5.607e-05,5.6351052631578935e-05,false,"roi"],["ETC/BTC",0.0,1516947000.0,1516958700.0,4622,195,0.00253806,0.0025507821052631577,false,"roi"],["ZEC/BTC",-0.0,1516951500.0,1516960500.0,4637,150,0.0415,0.04170802005012531,false,"roi"],["XLM/BTC",0.00997506,1516960500.0,1516962300.0,4667,30,5.321e-05,5.401015037593984e-05,false,"roi"],["XMR/BTC",-0.0,15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\ No newline at end of file diff --git a/scripts/plot_dataframe.py b/scripts/plot_dataframe.py index 6b954ac4c..581518b12 100755 --- a/scripts/plot_dataframe.py +++ b/scripts/plot_dataframe.py @@ -41,6 +41,7 @@ from plotly.offline import plot from freqtrade import persistence from freqtrade.arguments import Arguments, TimeRange from freqtrade.data import history +from freqtrade.data.btanalysis import load_backtest_data, BT_DATA_COLUMNS from freqtrade.exchange import Exchange from freqtrade.optimize.backtesting import setup_configuration from freqtrade.persistence import Trade @@ -56,7 +57,8 @@ def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFram trades: pd.DataFrame = pd.DataFrame() if args.db_url: persistence.init(_CONF) - columns = ["pair", "profit", "opents", "closets", "open_rate", "close_rate", "duration"] + columns = ["pair", "profit", "open_time", "close_time", + "open_rate", "close_rate", "duration"] for x in Trade.query.all(): print("date: {}".format(x.open_date)) @@ -71,33 +73,13 @@ def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFram columns=columns) elif args.exportfilename: - file = Path(args.exportfilename) - # must align with columns in backtest.py - columns = ["pair", "profit", "opents", "closets", "index", "duration", - "open_rate", "close_rate", "open_at_end", "sell_reason"] - if file.exists(): - with file.open() as f: - data = json.load(f) - trades = pd.DataFrame(data, columns=columns) - trades = trades.loc[trades["pair"] == pair] - if timerange: - if timerange.starttype == 'date': - trades = trades.loc[trades["opents"] >= timerange.startts] - if timerange.stoptype == 'date': - trades = trades.loc[trades["opents"] <= timerange.stopts] - trades['opents'] = pd.to_datetime( - trades['opents'], - unit='s', - utc=True, - infer_datetime_format=True) - trades['closets'] = pd.to_datetime( - trades['closets'], - unit='s', - utc=True, - infer_datetime_format=True) + file = Path(args.exportfilename) + if file.exists(): + load_backtest_data(file) + else: - trades = pd.DataFrame([], columns=columns) + trades = pd.DataFrame([], columns=BT_DATA_COLUMNS) return trades @@ -206,7 +188,7 @@ def extract_trades_of_period(dataframe, trades) -> pd.DataFrame: Compare trades and backtested pair DataFrames to get trades performed on backtested period :return: the DataFrame of a trades of period """ - trades = trades.loc[trades['opents'] >= dataframe.iloc[0]['date']] + trades = trades.loc[trades['open_time'] >= dataframe.iloc[0]['date']] return trades @@ -279,7 +261,7 @@ def generate_graph( ) trade_buys = go.Scattergl( - x=trades["opents"], + x=trades["open_time"], y=trades["open_rate"], mode='markers', name='trade_buy', @@ -291,7 +273,7 @@ def generate_graph( ) ) trade_sells = go.Scattergl( - x=trades["closets"], + x=trades["close_time"], y=trades["close_rate"], mode='markers', name='trade_sell',