Merge branch 'develop' into backtest_fitlivepredictions
This commit is contained in:
Wagner Costa
@@ -1271,7 +1271,7 @@ def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results, tmpdir):
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assert csv_file.is_file()
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line = csv_file.read_text()
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assert ('Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,"3,930.0 m",0.43662' in line
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or "Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,2 days 17:30:00,0.43662" in line)
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or "Best,1,2,-1.25%,-1.2222,-0.00125625,,-2.51,2 days 17:30:00,2,0,0.43662" in line)
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csv_file.unlink()
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@@ -2679,7 +2679,7 @@ def saved_hyperopt_results():
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'params_dict': {
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'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556}, # noqa: E501
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'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}}, # noqa: E501
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'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'}, # noqa: E501
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'results_metrics': {'total_trades': 2, 'trade_count_long': 2, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'}, # noqa: E501
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'results_explanation': ' 2 trades. Avg profit -1.25%. Total profit -0.00125625 BTC ( -2.51Σ%). Avg duration 3930.0 min.', # noqa: E501
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'total_profit': -0.00125625,
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'current_epoch': 1,
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@@ -2696,7 +2696,7 @@ def saved_hyperopt_results():
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'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, # noqa: E501
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'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0}, # noqa: E501
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'stoploss': {'stoploss': -0.338070047333259}},
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'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
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'results_metrics': {'total_trades': 1, 'trade_count_long': 1, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
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'results_explanation': ' 1 trades. Avg profit 0.12%. Total profit 0.00006185 BTC ( 0.12Σ%). Avg duration 1200.0 min.', # noqa: E501
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'total_profit': 6.185e-05,
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'current_epoch': 2,
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@@ -2707,7 +2707,7 @@ def saved_hyperopt_results():
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'loss': 14.241196856510731,
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'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611}, # noqa: E501
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'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}}, # noqa: E501
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'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'max_drawdown': 0.25, 'max_drawdown_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
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'results_metrics': {'total_trades': 621, 'trade_count_long': 621, 'trade_count_short': 0, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'max_drawdown': 0.25, 'max_drawdown_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
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'results_explanation': ' 621 trades. Avg profit -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.', # noqa: E501
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'total_profit': -0.13639474,
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'current_epoch': 3,
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@@ -2718,14 +2718,14 @@ def saved_hyperopt_results():
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'loss': 100000,
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'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478}, # noqa: E501
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'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}}, # noqa: E501
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'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()}, # noqa: E501
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'results_metrics': {'total_trades': 0, 'trade_count_long': 0, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()}, # noqa: E501
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'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
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'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_random': False, 'is_best': False # noqa: E501
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}, {
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'loss': 0.22195522184191518,
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'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
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'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}}, # noqa: E501
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'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'max_drawdown': 0.34, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
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'results_metrics': {'total_trades': 14, 'trade_count_long': 14, 'trade_count_short': 0, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'max_drawdown': 0.34, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
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'results_explanation': ' 14 trades. Avg profit -0.35%. Total profit -0.00248014 BTC ( -4.96Σ%). Avg duration 3402.9 min.', # noqa: E501
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'total_profit': -0.002480140000000001,
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'current_epoch': 5,
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@@ -2736,7 +2736,7 @@ def saved_hyperopt_results():
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'loss': 0.545315889154162,
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'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423}, # noqa: E501
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'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}}, # noqa: E501
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'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'max_drawdown': 0.45, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
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'results_metrics': {'total_trades': 39, 'trade_count_long': 39, 'trade_count_short': 0, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'max_drawdown': 0.45, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
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'results_explanation': ' 39 trades. Avg profit -0.21%. Total profit -0.00417730 BTC ( -8.35Σ%). Avg duration 636.9 min.', # noqa: E501
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'total_profit': -0.0041773,
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'current_epoch': 6,
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@@ -2749,7 +2749,7 @@ def saved_hyperopt_results():
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'params_details': {
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'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0}, # noqa: E501
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'stoploss': {'stoploss': -0.14613268022709905}}, # noqa: E501
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'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'max_drawdown': 0.50, 'max_drawdown_abs': -200.955321, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
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'results_metrics': {'total_trades': 318, 'trade_count_long': 318, 'trade_count_short': 0, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'max_drawdown': 0.50, 'max_drawdown_abs': -200.955321, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
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'results_explanation': ' 318 trades. Avg profit -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.', # noqa: E501
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'total_profit': -0.06339929,
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'current_epoch': 7,
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@@ -2760,7 +2760,7 @@ def saved_hyperopt_results():
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'loss': 20.0, # noqa: E501
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'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401}, # noqa: E501
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'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}}, # noqa: E501
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'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.52, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
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'results_metrics': {'total_trades': 1, 'trade_count_long': 1, 'trade_count_short': 0, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.52, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
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'results_explanation': ' 1 trades. Avg profit 0.00%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration 5340.0 min.', # noqa: E501
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'total_profit': 0.0,
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'current_epoch': 8,
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@@ -2771,7 +2771,7 @@ def saved_hyperopt_results():
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'loss': 2.4731817780991223,
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'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503}, # noqa: E501
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'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}}, # noqa: E501
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||||
'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'max_drawdown': 0.41, 'max_drawdown_abs': -150.955321, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 229, 'trade_count_long': 229, 'trade_count_short': 0, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'max_drawdown': 0.41, 'max_drawdown_abs': -150.955321, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
|
||||
'results_explanation': ' 229 trades. Avg profit -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.', # noqa: E501
|
||||
'total_profit': -0.044050070000000004, # noqa: E501
|
||||
'current_epoch': 9,
|
||||
@@ -2782,7 +2782,7 @@ def saved_hyperopt_results():
|
||||
'loss': -0.2604606005845212, # noqa: E501
|
||||
'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'max_drawdown': 0.13, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 4, 'trade_count_long': 4, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'max_drawdown': 0.13, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
|
||||
'results_explanation': ' 4 trades. Avg profit 0.11%. Total profit 0.00021629 BTC ( 0.43Σ%). Avg duration 2850.0 min.', # noqa: E501
|
||||
'total_profit': 0.00021629,
|
||||
'current_epoch': 10,
|
||||
@@ -2794,7 +2794,7 @@ def saved_hyperopt_results():
|
||||
'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}}, # noqa: E501
|
||||
# New Hyperopt mode!
|
||||
'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'max_drawdown': 0.52, 'max_drawdown_abs': -224.955321, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 117, 'trade_count_long': 117, 'trade_count_short': 0, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'max_drawdown': 0.52, 'max_drawdown_abs': -224.955321, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
|
||||
'results_explanation': ' 117 trades. Avg profit -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.', # noqa: E501
|
||||
'total_profit': -0.07436117,
|
||||
'current_epoch': 11,
|
||||
@@ -2805,7 +2805,7 @@ def saved_hyperopt_results():
|
||||
'loss': 100000,
|
||||
'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 0, 'trade_count_long': 0, 'trade_count_short': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
|
||||
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
|
||||
'total_profit': 0,
|
||||
'current_epoch': 12,
|
||||
|
||||
@@ -1207,12 +1207,17 @@ def test_create_dry_run_order_fees(
|
||||
assert order1['fee']['rate'] == fee
|
||||
|
||||
|
||||
@pytest.mark.parametrize("side,startprice,endprice", [
|
||||
("buy", 25.563, 25.566),
|
||||
("sell", 25.566, 25.563)
|
||||
@pytest.mark.parametrize("side,price,filled", [
|
||||
# order_book_l2_usd spread:
|
||||
# best ask: 25.566
|
||||
# best bid: 25.563
|
||||
("buy", 25.563, False),
|
||||
("buy", 25.566, True),
|
||||
("sell", 25.566, False),
|
||||
("sell", 25.563, True),
|
||||
])
|
||||
@pytest.mark.parametrize("exchange_name", EXCHANGES)
|
||||
def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice, endprice,
|
||||
def test_create_dry_run_order_limit_fill(default_conf, mocker, side, price, filled,
|
||||
exchange_name, order_book_l2_usd):
|
||||
default_conf['dry_run'] = True
|
||||
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
|
||||
@@ -1226,7 +1231,7 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice,
|
||||
ordertype='limit',
|
||||
side=side,
|
||||
amount=1,
|
||||
rate=startprice,
|
||||
rate=price,
|
||||
leverage=1.0
|
||||
)
|
||||
assert order_book_l2_usd.call_count == 1
|
||||
@@ -1235,22 +1240,17 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice,
|
||||
assert order["side"] == side
|
||||
assert order["type"] == "limit"
|
||||
assert order["symbol"] == "LTC/USDT"
|
||||
assert order["average"] == price
|
||||
assert order['status'] == 'open' if not filled else 'closed'
|
||||
order_book_l2_usd.reset_mock()
|
||||
|
||||
# fetch order again...
|
||||
order_closed = exchange.fetch_dry_run_order(order['id'])
|
||||
assert order_book_l2_usd.call_count == 1
|
||||
assert order_closed['status'] == 'open'
|
||||
assert not order['fee']
|
||||
assert order_closed['filled'] == 0
|
||||
assert order_book_l2_usd.call_count == (1 if not filled else 0)
|
||||
assert order_closed['status'] == ('open' if not filled else 'closed')
|
||||
assert order_closed['filled'] == (0 if not filled else 1)
|
||||
|
||||
order_book_l2_usd.reset_mock()
|
||||
order_closed['price'] = endprice
|
||||
|
||||
order_closed = exchange.fetch_dry_run_order(order['id'])
|
||||
assert order_closed['status'] == 'closed'
|
||||
assert order['fee']
|
||||
assert order_closed['filled'] == 1
|
||||
assert order_closed['filled'] == order_closed['amount']
|
||||
|
||||
# Empty orderbook test
|
||||
mocker.patch('freqtrade.exchange.Exchange.fetch_l2_order_book',
|
||||
|
||||
@@ -27,10 +27,9 @@ def freqai_conf(default_conf, tmpdir):
|
||||
"timerange": "20180110-20180115",
|
||||
"freqai": {
|
||||
"enabled": True,
|
||||
"startup_candles": 10000,
|
||||
"purge_old_models": True,
|
||||
"train_period_days": 2,
|
||||
"backtest_period_days": 2,
|
||||
"backtest_period_days": 10,
|
||||
"live_retrain_hours": 0,
|
||||
"expiration_hours": 1,
|
||||
"identifier": "uniqe-id100",
|
||||
@@ -58,6 +57,30 @@ def freqai_conf(default_conf, tmpdir):
|
||||
return freqaiconf
|
||||
|
||||
|
||||
def make_rl_config(conf):
|
||||
conf.update({"strategy": "freqai_rl_test_strat"})
|
||||
conf["freqai"].update({"model_training_parameters": {
|
||||
"learning_rate": 0.00025,
|
||||
"gamma": 0.9,
|
||||
"verbose": 1
|
||||
}})
|
||||
conf["freqai"]["rl_config"] = {
|
||||
"train_cycles": 1,
|
||||
"thread_count": 2,
|
||||
"max_trade_duration_candles": 300,
|
||||
"model_type": "PPO",
|
||||
"policy_type": "MlpPolicy",
|
||||
"max_training_drawdown_pct": 0.5,
|
||||
"net_arch": [32, 32],
|
||||
"model_reward_parameters": {
|
||||
"rr": 1,
|
||||
"profit_aim": 0.02,
|
||||
"win_reward_factor": 2
|
||||
}}
|
||||
|
||||
return conf
|
||||
|
||||
|
||||
def get_patched_data_kitchen(mocker, freqaiconf):
|
||||
dk = FreqaiDataKitchen(freqaiconf)
|
||||
return dk
|
||||
|
||||
@@ -13,8 +13,8 @@ from freqtrade.freqai.utils import download_all_data_for_training, get_required_
|
||||
from freqtrade.optimize.backtesting import Backtesting
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.plugins.pairlistmanager import PairListManager
|
||||
from tests.conftest import get_patched_exchange, log_has_re
|
||||
from tests.freqai.conftest import get_patched_freqai_strategy
|
||||
from tests.conftest import create_mock_trades, get_patched_exchange, log_has_re
|
||||
from tests.freqai.conftest import get_patched_freqai_strategy, make_rl_config
|
||||
|
||||
|
||||
def is_arm() -> bool:
|
||||
@@ -32,11 +32,17 @@ def is_mac() -> bool:
|
||||
('XGBoostRegressor', False, True, False),
|
||||
('XGBoostRFRegressor', False, False, False),
|
||||
('CatboostRegressor', False, False, False),
|
||||
('ReinforcementLearner', False, True, False),
|
||||
('ReinforcementLearner_multiproc', False, False, False),
|
||||
('ReinforcementLearner_test_4ac', False, False, False)
|
||||
])
|
||||
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32):
|
||||
if is_arm() and model == 'CatboostRegressor':
|
||||
pytest.skip("CatBoost is not supported on ARM")
|
||||
|
||||
if is_mac() and 'Reinforcement' in model:
|
||||
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
|
||||
|
||||
model_save_ext = 'joblib'
|
||||
freqai_conf.update({"freqaimodel": model})
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
@@ -45,6 +51,26 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
|
||||
freqai_conf['freqai']['feature_parameters'].update({"use_DBSCAN_to_remove_outliers": dbscan})
|
||||
freqai_conf.update({"reduce_df_footprint": float32})
|
||||
|
||||
if 'ReinforcementLearner' in model:
|
||||
model_save_ext = 'zip'
|
||||
freqai_conf = make_rl_config(freqai_conf)
|
||||
# test the RL guardrails
|
||||
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
|
||||
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
|
||||
|
||||
if 'test_4ac' in model:
|
||||
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
|
||||
|
||||
if 'ReinforcementLearner' in model:
|
||||
model_save_ext = 'zip'
|
||||
freqai_conf = make_rl_config(freqai_conf)
|
||||
# test the RL guardrails
|
||||
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
|
||||
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
|
||||
|
||||
if 'test_4ac' in model:
|
||||
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
@@ -52,6 +78,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
freqai.dk.set_paths('ADA/BTC', 10000)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
@@ -165,25 +192,35 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
|
||||
@pytest.mark.parametrize(
|
||||
"model, num_files, strat",
|
||||
[
|
||||
("LightGBMRegressor", 6, "freqai_test_strat"),
|
||||
("XGBoostRegressor", 6, "freqai_test_strat"),
|
||||
("CatboostRegressor", 6, "freqai_test_strat"),
|
||||
("XGBoostClassifier", 6, "freqai_test_classifier"),
|
||||
("LightGBMClassifier", 6, "freqai_test_classifier"),
|
||||
("CatboostClassifier", 6, "freqai_test_classifier")
|
||||
("LightGBMRegressor", 2, "freqai_test_strat"),
|
||||
("XGBoostRegressor", 2, "freqai_test_strat"),
|
||||
("CatboostRegressor", 2, "freqai_test_strat"),
|
||||
("ReinforcementLearner", 3, "freqai_rl_test_strat"),
|
||||
("XGBoostClassifier", 2, "freqai_test_classifier"),
|
||||
("LightGBMClassifier", 2, "freqai_test_classifier"),
|
||||
("CatboostClassifier", 2, "freqai_test_classifier")
|
||||
],
|
||||
)
|
||||
def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
|
||||
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
|
||||
freqai_conf['runmode'] = RunMode.BACKTEST
|
||||
Trade.use_db = False
|
||||
if is_arm() and "Catboost" in model:
|
||||
pytest.skip("CatBoost is not supported on ARM")
|
||||
|
||||
if is_mac() and 'Reinforcement' in model:
|
||||
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
|
||||
Trade.use_db = False
|
||||
|
||||
freqai_conf.update({"freqaimodel": model})
|
||||
freqai_conf.update({"timerange": "20180120-20180130"})
|
||||
freqai_conf.update({"strategy": strat})
|
||||
|
||||
if 'ReinforcementLearner' in model:
|
||||
freqai_conf = make_rl_config(freqai_conf)
|
||||
|
||||
if 'test_4ac' in model:
|
||||
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
@@ -207,6 +244,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
assert len(model_folders) == num_files
|
||||
Trade.use_db = True
|
||||
assert log_has_re(
|
||||
"Removed features ",
|
||||
caplog,
|
||||
@@ -269,7 +307,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
assert len(model_folders) == 6
|
||||
assert len(model_folders) == 2
|
||||
|
||||
# without deleting the existing folder structure, re-run
|
||||
|
||||
@@ -507,3 +545,43 @@ def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmpdir):
|
||||
"Downloading",
|
||||
caplog,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.usefixtures("init_persistence")
|
||||
@pytest.mark.parametrize('dp_exists', [(False), (True)])
|
||||
def test_get_state_info(mocker, freqai_conf, dp_exists, caplog, tickers):
|
||||
|
||||
if is_mac():
|
||||
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
|
||||
|
||||
freqai_conf.update({"freqaimodel": "ReinforcementLearner"})
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"strategy": "freqai_rl_test_strat"})
|
||||
freqai_conf = make_rl_config(freqai_conf)
|
||||
freqai_conf['entry_pricing']['price_side'] = 'same'
|
||||
freqai_conf['exit_pricing']['price_side'] = 'same'
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
ticker_mock = MagicMock(return_value=tickers()['ETH/BTC'])
|
||||
mocker.patch("freqtrade.exchange.Exchange.fetch_ticker", ticker_mock)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
|
||||
if not dp_exists:
|
||||
strategy.dp._exchange = None
|
||||
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.data_provider = strategy.dp
|
||||
freqai.live = True
|
||||
|
||||
Trade.use_db = True
|
||||
create_mock_trades(MagicMock(return_value=0.0025), False, True)
|
||||
freqai.get_state_info("ADA/BTC")
|
||||
freqai.get_state_info("ETH/BTC")
|
||||
|
||||
if not dp_exists:
|
||||
assert log_has_re(
|
||||
"No exchange available",
|
||||
caplog,
|
||||
)
|
||||
|
||||
66
tests/freqai/test_models/ReinforcementLearner_test_4ac.py
Normal file
66
tests/freqai/test_models/ReinforcementLearner_test_4ac.py
Normal file
@@ -0,0 +1,66 @@
|
||||
import logging
|
||||
|
||||
import numpy as np
|
||||
|
||||
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
|
||||
from freqtrade.freqai.RL.Base4ActionRLEnv import Actions, Base4ActionRLEnv, Positions
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ReinforcementLearner_test_4ac(ReinforcementLearner):
|
||||
"""
|
||||
User created Reinforcement Learning Model prediction model.
|
||||
"""
|
||||
|
||||
class MyRLEnv(Base4ActionRLEnv):
|
||||
"""
|
||||
User can override any function in BaseRLEnv and gym.Env. Here the user
|
||||
sets a custom reward based on profit and trade duration.
|
||||
"""
|
||||
|
||||
def calculate_reward(self, action: int) -> float:
|
||||
|
||||
# first, penalize if the action is not valid
|
||||
if not self._is_valid(action):
|
||||
return -2
|
||||
|
||||
pnl = self.get_unrealized_profit()
|
||||
rew = np.sign(pnl) * (pnl + 1)
|
||||
factor = 100.
|
||||
|
||||
# reward agent for entering trades
|
||||
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
|
||||
and self._position == Positions.Neutral):
|
||||
return 25
|
||||
# discourage agent from not entering trades
|
||||
if action == Actions.Neutral.value and self._position == Positions.Neutral:
|
||||
return -1
|
||||
|
||||
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
|
||||
trade_duration = self._current_tick - self._last_trade_tick # type: ignore
|
||||
|
||||
if trade_duration <= max_trade_duration:
|
||||
factor *= 1.5
|
||||
elif trade_duration > max_trade_duration:
|
||||
factor *= 0.5
|
||||
|
||||
# discourage sitting in position
|
||||
if (self._position in (Positions.Short, Positions.Long) and
|
||||
action == Actions.Neutral.value):
|
||||
return -1 * trade_duration / max_trade_duration
|
||||
|
||||
# close long
|
||||
if action == Actions.Exit.value and self._position == Positions.Long:
|
||||
if pnl > self.profit_aim * self.rr:
|
||||
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
|
||||
return float(rew * factor)
|
||||
|
||||
# close short
|
||||
if action == Actions.Exit.value and self._position == Positions.Short:
|
||||
if pnl > self.profit_aim * self.rr:
|
||||
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
|
||||
return float(rew * factor)
|
||||
|
||||
return 0.
|
||||
@@ -663,30 +663,9 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
|
||||
'', # Exit Signal Name
|
||||
|
||||
]
|
||||
row_detail = pd.DataFrame(
|
||||
[
|
||||
[
|
||||
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0, tzinfo=timezone.utc),
|
||||
200, 200.1, 197, 199, 1, 0, 0, 0, '', '', '',
|
||||
], [
|
||||
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=1, tzinfo=timezone.utc),
|
||||
199, 199.7, 199, 199.5, 0, 0, 0, 0, '', '', '',
|
||||
], [
|
||||
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=2, tzinfo=timezone.utc),
|
||||
199.5, 200.8, 199, 200.9, 0, 0, 0, 0, '', '', '',
|
||||
], [
|
||||
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=3, tzinfo=timezone.utc),
|
||||
200.5, 210.5, 193, 210.5, 0, 0, 0, 0, '', '', '', # ROI sell (?)
|
||||
], [
|
||||
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=4, tzinfo=timezone.utc),
|
||||
200, 200.1, 193, 199, 0, 0, 0, 0, '', '', '',
|
||||
],
|
||||
], columns=['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
|
||||
'enter_short', 'exit_short', 'long_tag', 'short_tag', 'exit_tag']
|
||||
)
|
||||
|
||||
# No data available.
|
||||
res = backtesting._get_exit_trade_entry(trade, row_sell)
|
||||
res = backtesting._get_exit_trade_entry(trade, row_sell, True)
|
||||
assert res is not None
|
||||
assert res.exit_reason == ExitType.ROI.value
|
||||
assert res.close_date_utc == datetime(2020, 1, 1, 5, 0, tzinfo=timezone.utc)
|
||||
@@ -699,20 +678,9 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
|
||||
[], columns=['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
|
||||
'enter_short', 'exit_short', 'long_tag', 'short_tag', 'exit_tag'])
|
||||
|
||||
res = backtesting._get_exit_trade_entry(trade, row)
|
||||
res = backtesting._get_exit_trade_entry(trade, row, True)
|
||||
assert res is None
|
||||
|
||||
# Assign backtest-detail data
|
||||
backtesting.detail_data[pair] = row_detail
|
||||
|
||||
res = backtesting._get_exit_trade_entry(trade, row_sell)
|
||||
assert res is not None
|
||||
assert res.exit_reason == ExitType.ROI.value
|
||||
# Sell at minute 3 (not available above!)
|
||||
assert res.close_date_utc == datetime(2020, 1, 1, 5, 3, tzinfo=timezone.utc)
|
||||
sell_order = res.select_order('sell', True)
|
||||
assert sell_order is not None
|
||||
|
||||
|
||||
def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
|
||||
default_conf['use_exit_signal'] = False
|
||||
@@ -788,17 +756,98 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
|
||||
for _, t in results.iterrows():
|
||||
assert len(t['orders']) == 2
|
||||
ln = data_pair.loc[data_pair["date"] == t["open_date"]]
|
||||
# Check open trade rate alignes to open rate
|
||||
# Check open trade rate aligns to open rate
|
||||
assert not ln.empty
|
||||
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
|
||||
# check close trade rate alignes to close rate or is between high and low
|
||||
# check close trade rate aligns to close rate or is between high and low
|
||||
ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
|
||||
assert not ln1.empty
|
||||
assert (round(ln1.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
|
||||
round(ln1.iloc[0]["low"], 6) < round(
|
||||
t["close_rate"], 6) < round(ln1.iloc[0]["high"], 6))
|
||||
|
||||
|
||||
@pytest.mark.parametrize('use_detail', [True, False])
|
||||
def test_backtest_one_detail(default_conf_usdt, fee, mocker, testdatadir, use_detail) -> None:
|
||||
default_conf_usdt['use_exit_signal'] = False
|
||||
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
|
||||
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
|
||||
mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
|
||||
if use_detail:
|
||||
default_conf_usdt['timeframe_detail'] = '1m'
|
||||
patch_exchange(mocker)
|
||||
|
||||
def advise_entry(df, *args, **kwargs):
|
||||
# Mock function to force several entries
|
||||
df.loc[(df['rsi'] < 40), 'enter_long'] = 1
|
||||
return df
|
||||
|
||||
def custom_entry_price(proposed_rate, **kwargs):
|
||||
return proposed_rate * 0.997
|
||||
|
||||
backtesting = Backtesting(default_conf_usdt)
|
||||
backtesting._set_strategy(backtesting.strategylist[0])
|
||||
backtesting.strategy.populate_entry_trend = advise_entry
|
||||
backtesting.strategy.custom_entry_price = custom_entry_price
|
||||
pair = 'XRP/ETH'
|
||||
# Pick a timerange adapted to the pair we use to test
|
||||
timerange = TimeRange.parse_timerange('20191010-20191013')
|
||||
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['XRP/ETH'],
|
||||
timerange=timerange)
|
||||
if use_detail:
|
||||
data_1m = history.load_data(datadir=testdatadir, timeframe='1m', pairs=['XRP/ETH'],
|
||||
timerange=timerange)
|
||||
backtesting.detail_data = data_1m
|
||||
processed = backtesting.strategy.advise_all_indicators(data)
|
||||
min_date, max_date = get_timerange(processed)
|
||||
|
||||
result = backtesting.backtest(
|
||||
processed=deepcopy(processed),
|
||||
start_date=min_date,
|
||||
end_date=max_date,
|
||||
max_open_trades=10,
|
||||
)
|
||||
results = result['results']
|
||||
assert not results.empty
|
||||
# Timeout settings from default_conf = entry: 10, exit: 30
|
||||
assert len(results) == (2 if use_detail else 3)
|
||||
|
||||
assert 'orders' in results.columns
|
||||
data_pair = processed[pair]
|
||||
|
||||
data_1m_pair = data_1m[pair] if use_detail else pd.DataFrame()
|
||||
late_entry = 0
|
||||
for _, t in results.iterrows():
|
||||
assert len(t['orders']) == 2
|
||||
|
||||
entryo = t['orders'][0]
|
||||
entry_ts = datetime.fromtimestamp(entryo['order_filled_timestamp'] // 1000, tz=timezone.utc)
|
||||
if entry_ts > t['open_date']:
|
||||
late_entry += 1
|
||||
|
||||
# Get "entry fill" candle
|
||||
ln = (data_1m_pair.loc[data_1m_pair["date"] == entry_ts]
|
||||
if use_detail else data_pair.loc[data_pair["date"] == entry_ts])
|
||||
# Check open trade rate aligns to open rate
|
||||
assert not ln.empty
|
||||
|
||||
# assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
|
||||
assert round(ln.iloc[0]["low"], 6) <= round(
|
||||
t["open_rate"], 6) <= round(ln.iloc[0]["high"], 6)
|
||||
# check close trade rate aligns to close rate or is between high and low
|
||||
ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
|
||||
if use_detail:
|
||||
ln1_1m = data_1m_pair.loc[data_1m_pair["date"] == t["close_date"]]
|
||||
assert not ln1.empty or not ln1_1m.empty
|
||||
else:
|
||||
assert not ln1.empty
|
||||
ln2 = ln1_1m if ln1.empty else ln1
|
||||
|
||||
assert (round(ln2.iloc[0]["low"], 6) <= round(
|
||||
t["close_rate"], 6) <= round(ln2.iloc[0]["high"], 6))
|
||||
|
||||
assert late_entry > 0
|
||||
|
||||
|
||||
def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:
|
||||
# This strategy intentionally places unfillable orders.
|
||||
default_conf['strategy'] = 'StrategyTestV3CustomEntryPrice'
|
||||
|
||||
@@ -57,7 +57,10 @@ def botclient(default_conf, mocker):
|
||||
try:
|
||||
apiserver = ApiServer(default_conf)
|
||||
apiserver.add_rpc_handler(rpc)
|
||||
yield ftbot, TestClient(apiserver.app)
|
||||
# We need to use the TestClient as a context manager to
|
||||
# handle lifespan events correctly
|
||||
with TestClient(apiserver.app) as client:
|
||||
yield ftbot, client
|
||||
# Cleanup ... ?
|
||||
finally:
|
||||
if apiserver:
|
||||
@@ -438,7 +441,6 @@ def test_api_cleanup(default_conf, mocker, caplog):
|
||||
apiserver.cleanup()
|
||||
assert apiserver._server.cleanup.call_count == 1
|
||||
assert log_has("Stopping API Server", caplog)
|
||||
assert log_has("Stopping API Server background tasks", caplog)
|
||||
ApiServer.shutdown()
|
||||
|
||||
|
||||
@@ -1459,6 +1461,7 @@ def test_api_strategies(botclient, tmpdir):
|
||||
'StrategyTestV3',
|
||||
'StrategyTestV3CustomEntryPrice',
|
||||
'StrategyTestV3Futures',
|
||||
'freqai_rl_test_strat',
|
||||
'freqai_test_classifier',
|
||||
'freqai_test_multimodel_classifier_strat',
|
||||
'freqai_test_multimodel_strat',
|
||||
@@ -1714,12 +1717,14 @@ def test_api_ws_subscribe(botclient, mocker):
|
||||
|
||||
with client.websocket_connect(ws_url) as ws:
|
||||
ws.send_json({'type': 'subscribe', 'data': ['whitelist']})
|
||||
time.sleep(1)
|
||||
|
||||
# Check call count is now 1 as we sent a valid subscribe request
|
||||
assert sub_mock.call_count == 1
|
||||
|
||||
with client.websocket_connect(ws_url) as ws:
|
||||
ws.send_json({'type': 'subscribe', 'data': 'whitelist'})
|
||||
time.sleep(1)
|
||||
|
||||
# Call count hasn't changed as the subscribe request was invalid
|
||||
assert sub_mock.call_count == 1
|
||||
@@ -1773,24 +1778,18 @@ def test_api_ws_send_msg(default_conf, mocker, caplog):
|
||||
mocker.patch('freqtrade.rpc.api_server.ApiServer.start_api')
|
||||
apiserver = ApiServer(default_conf)
|
||||
apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf)))
|
||||
apiserver.start_message_queue()
|
||||
# Give the queue thread time to start
|
||||
time.sleep(0.2)
|
||||
|
||||
# Test message_queue coro receives the message
|
||||
test_message = {"type": "status", "data": "test"}
|
||||
apiserver.send_msg(test_message)
|
||||
time.sleep(0.1) # Not sure how else to wait for the coro to receive the data
|
||||
assert log_has("Found message of type: status", caplog)
|
||||
# Start test client context manager to run lifespan events
|
||||
with TestClient(apiserver.app):
|
||||
# Test message is published on the Message Stream
|
||||
test_message = {"type": "status", "data": "test"}
|
||||
first_waiter = apiserver._message_stream._waiter
|
||||
apiserver.send_msg(test_message)
|
||||
assert first_waiter.result()[0] == test_message
|
||||
|
||||
# Test if exception logged when error occurs in sending
|
||||
mocker.patch('freqtrade.rpc.api_server.ws.channel.ChannelManager.broadcast',
|
||||
side_effect=Exception)
|
||||
|
||||
apiserver.send_msg(test_message)
|
||||
time.sleep(0.1) # Not sure how else to wait for the coro to receive the data
|
||||
assert log_has_re(r"Exception happened in background task.*", caplog)
|
||||
second_waiter = apiserver._message_stream._waiter
|
||||
apiserver.send_msg(test_message)
|
||||
assert first_waiter != second_waiter
|
||||
|
||||
finally:
|
||||
apiserver.cleanup()
|
||||
ApiServer.shutdown()
|
||||
|
||||
105
tests/strategy/strats/freqai_rl_test_strat.py
Normal file
105
tests/strategy/strats/freqai_rl_test_strat.py
Normal file
@@ -0,0 +1,105 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import IStrategy, merge_informative_pair
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class freqai_rl_test_strat(IStrategy):
|
||||
"""
|
||||
Test strategy - used for testing freqAI functionalities.
|
||||
DO not use in production.
|
||||
"""
|
||||
|
||||
minimal_roi = {"0": 0.1, "240": -1}
|
||||
|
||||
process_only_new_candles = True
|
||||
stoploss = -0.05
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 30
|
||||
can_short = False
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
|
||||
t = int(t)
|
||||
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
|
||||
# The following columns are necessary for RL models.
|
||||
informative[f"%-{pair}raw_close"] = informative["close"]
|
||||
informative[f"%-{pair}raw_open"] = informative["open"]
|
||||
informative[f"%-{pair}raw_high"] = informative["high"]
|
||||
informative[f"%-{pair}raw_low"] = informative["low"]
|
||||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
# For RL, there are no direct targets to set. This is filler (neutral)
|
||||
# until the agent sends an action.
|
||||
df["&-action"] = 0
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
|
||||
] = (1, "short")
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
|
||||
if exit_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
|
||||
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
|
||||
if exit_short_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
|
||||
|
||||
return df
|
||||
@@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
|
||||
directory = Path(__file__).parent / "strats"
|
||||
strategies = StrategyResolver._search_all_objects(directory, enum_failed=False)
|
||||
assert isinstance(strategies, list)
|
||||
assert len(strategies) == 11
|
||||
assert len(strategies) == 12
|
||||
assert isinstance(strategies[0], dict)
|
||||
|
||||
|
||||
@@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
|
||||
directory = Path(__file__).parent / "strats"
|
||||
strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
|
||||
assert isinstance(strategies, list)
|
||||
assert len(strategies) == 12
|
||||
assert len(strategies) == 13
|
||||
# with enum_failed=True search_all_objects() shall find 2 good strategies
|
||||
# and 1 which fails to load
|
||||
assert len([x for x in strategies if x['class'] is not None]) == 11
|
||||
assert len([x for x in strategies if x['class'] is not None]) == 12
|
||||
|
||||
assert len([x for x in strategies if x['class'] is None]) == 1
|
||||
|
||||
|
||||
@@ -1498,6 +1498,7 @@ def test_handle_stoploss_on_exchange_trailing(
|
||||
})
|
||||
)
|
||||
assert freqtrade.handle_trade(trade) is True
|
||||
assert trade.stoploss_order_id is None
|
||||
|
||||
|
||||
@pytest.mark.parametrize("is_short", [False, True])
|
||||
@@ -5046,7 +5047,7 @@ def test_startup_backpopulate_precision(mocker, default_conf_usdt, fee, caplog):
|
||||
|
||||
@pytest.mark.usefixtures("init_persistence")
|
||||
@pytest.mark.parametrize("is_short", [False, True])
|
||||
def test_update_closed_trades_without_assigned_fees(mocker, default_conf_usdt, fee, is_short):
|
||||
def test_update_trades_without_assigned_fees(mocker, default_conf_usdt, fee, is_short):
|
||||
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
|
||||
|
||||
def patch_with_fee(order):
|
||||
@@ -5075,7 +5076,7 @@ def test_update_closed_trades_without_assigned_fees(mocker, default_conf_usdt, f
|
||||
assert trade.fee_close_cost is None
|
||||
assert trade.fee_close_currency is None
|
||||
|
||||
freqtrade.update_closed_trades_without_assigned_fees()
|
||||
freqtrade.update_trades_without_assigned_fees()
|
||||
|
||||
# Does nothing for dry-run
|
||||
trades = Trade.get_trades().all()
|
||||
@@ -5088,7 +5089,7 @@ def test_update_closed_trades_without_assigned_fees(mocker, default_conf_usdt, f
|
||||
|
||||
freqtrade.config['dry_run'] = False
|
||||
|
||||
freqtrade.update_closed_trades_without_assigned_fees()
|
||||
freqtrade.update_trades_without_assigned_fees()
|
||||
|
||||
trades = Trade.get_trades().all()
|
||||
assert len(trades) == MOCK_TRADE_COUNT
|
||||
@@ -5551,7 +5552,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
|
||||
assert trade.stake_amount == 110
|
||||
|
||||
# Assume it does nothing since order is closed and trade is open
|
||||
freqtrade.update_closed_trades_without_assigned_fees()
|
||||
freqtrade.update_trades_without_assigned_fees()
|
||||
|
||||
trade = Trade.query.first()
|
||||
assert trade
|
||||
@@ -5622,7 +5623,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
|
||||
mocker.patch('freqtrade.exchange.Exchange.create_order', fetch_order_mm)
|
||||
mocker.patch('freqtrade.exchange.Exchange.fetch_order', fetch_order_mm)
|
||||
mocker.patch('freqtrade.exchange.Exchange.fetch_order_or_stoploss_order', fetch_order_mm)
|
||||
freqtrade.update_closed_trades_without_assigned_fees()
|
||||
freqtrade.update_trades_without_assigned_fees()
|
||||
|
||||
orders = Order.query.all()
|
||||
assert orders
|
||||
@@ -5839,7 +5840,7 @@ def test_position_adjust2(mocker, default_conf_usdt, fee) -> None:
|
||||
assert trade.stake_amount == bid * amount
|
||||
|
||||
# Assume it does nothing since order is closed and trade is open
|
||||
freqtrade.update_closed_trades_without_assigned_fees()
|
||||
freqtrade.update_trades_without_assigned_fees()
|
||||
|
||||
trade = Trade.query.first()
|
||||
assert trade
|
||||
|
||||
10
tests/testdata/strategy_SampleStrategy.fthypt
vendored
10
tests/testdata/strategy_SampleStrategy.fthypt
vendored
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user