┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃ ┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ │ Empty │ 59 │ 3.30 │ 4263.790 │ 426.38 │ 6 days, 23:28:00 │ 33 0 26 │ 603.373 USDT 18.82% │
This commit is contained in:
Jérôme Delacotte
561
Empty.py
561
Empty.py
@@ -39,19 +39,24 @@ timeperiods = [3, 5, 12, 24, 36, 48, 60]
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long_timeperiods = [80, 100, 120, 140, 160, 180, 200]
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sma_indicators = list()
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sma_indicators_h = list()
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sma_indicators_d = list()
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score_indicators = list()
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stop_buying_indicators = list()
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god_genes_with_timeperiod = list()
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sma_deriv1_indicators = list()
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for timeperiod in timeperiods:
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sma_indicators.append(f"sma{timeperiod}")
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sma_indicators_h.append(f"sma{timeperiod}")
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sma_indicators_d.append(f"sma{timeperiod}")
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sma_indicators.append(f"sma{timeperiod}_1d")
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# god_genes_with_timeperiod.append(f'max{timeperiod}')
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# god_genes_with_timeperiod.append(f'min{timeperiod}')
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# god_genes_with_timeperiod.append(f"percent{timeperiod}")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_deriv1")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_deriv2")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_score")
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# sma_deriv1_indicators.append(f"sma{timeperiod}_deriv1")
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sma_deriv1_indicators.append(f"sma{timeperiod}_deriv1")
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# sma_deriv1_indicators.append(f"sma{timeperiod}_deriv2")
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# sma_deriv1_indicators.append(f"sma{timeperiod}_score")
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# stoploss_indicators.append(f"stop_buying{timeperiod}")
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stop_buying_indicators.append(f"stop_buying{timeperiod}_1d")
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@@ -63,29 +68,10 @@ for timeperiod in timeperiods:
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_down")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_change_up")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_change_down")
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for timeperiod in long_timeperiods:
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sma_deriv1_indicators.append(f"sma{timeperiod}_deriv1_1d")
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sma_indicators_d.append(f"sma{timeperiod}")
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operators = [
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"D", # Disabled gene
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">", # Indicator, bigger than cross indicator
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"<", # Indicator, smaller than cross indicator
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"=", # Indicator, equal with cross indicator
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"C", # Indicator, crossed the cross indicator
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"CA", # Indicator, crossed above the cross indicator
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"CB", # Indicator, crossed below the cross indicator
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# ">R", # Normalized indicator, bigger than real number
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# "=R", # Normalized indicator, equal with real number
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# "<R", # Normalized indicator, smaller than real number
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# "/>R", # Normalized indicator devided to cross indicator, bigger than real number
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# "/=R", # Normalized indicator devided to cross indicator, equal with real number
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# "/<R", # Normalized indicator devided to cross indicator, smaller than real number
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# "UT", # Indicator, is in UpTrend status
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# "DT", # Indicator, is in DownTrend status
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# "OT", # Indicator, is in Off trend status(RANGE)
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# "CUT", # Indicator, Entered to UpTrend status
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# "CDT", # Indicator, Entered to DownTrend status
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# "COT" # Indicator, Entered to Off trend status(RANGE)
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]
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# number of candles to check up,don,off trend.
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TREND_CHECK_CANDLES = 4
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DECIMALS = 1
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@@ -94,169 +80,6 @@ def normalize(df):
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df = (df-df.min())/(df.max()-df.min())
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return df
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def gene_calculator(dataframe, indicator):
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# print(indicator)
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# Cuz Timeperiods not effect calculating CDL patterns recognations
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if 'CDL' in indicator:
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splited_indicator = indicator.split('-')
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splited_indicator[1] = "0"
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new_indicator = "-".join(splited_indicator)
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# print(indicator, new_indicator)
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indicator = new_indicator
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gene = indicator.split("-")
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gene_name = gene[0]
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gene_len = len(gene)
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# print(f"GENE {gene_name} {gene_len} {indicator}")
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if gene_name in dataframe.keys():
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# print(f"{indicator}, calculated befoure")
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# print(len(dataframe.keys()))
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return dataframe[gene_name]
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if indicator in dataframe.keys():
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# print(f"{indicator}, calculated befoure")
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# print(len(dataframe.keys()))
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return dataframe[indicator]
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else:
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result = None
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# For Pattern Recognations
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if gene_len == 1:
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# print('gene_len == 1\t', indicator)
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result = getattr(talib, gene_name)(
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dataframe
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)
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return normalize(result)
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elif gene_len == 2:
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# print('gene_len == 2\t', indicator)
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gene_timeperiod = int(gene[1])
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result = getattr(talib, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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)
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return normalize(result)
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# For
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elif gene_len == 3:
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# print('gene_len == 3\t', indicator)
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gene_timeperiod = int(gene[2])
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gene_index = int(gene[1])
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result = getattr(talib, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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).iloc[:, gene_index]
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return normalize(result)
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# For trend operators(MA-5-SMA-4)
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elif gene_len == 4:
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# print('gene_len == 4\t', indicator)
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gene_timeperiod = int(gene[1])
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sharp_indicator = f'{gene_name}-{gene_timeperiod}'
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dataframe[sharp_indicator] = getattr(talib, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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)
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return normalize(talib.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
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# For trend operators(STOCH-0-4-SMA-4)
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elif gene_len == 5:
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# print('gene_len == 5\t', indicator)
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gene_timeperiod = int(gene[2])
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gene_index = int(gene[1])
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sharp_indicator = f'{gene_name}-{gene_index}-{gene_timeperiod}'
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dataframe[sharp_indicator] = getattr(talib, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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).iloc[:, gene_index]
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return normalize(talib.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
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def condition_generator(dataframe, operator, indicator, crossed_indicator, real_num):
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condition = (dataframe['volume'] > 10)
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# TODO : it ill callculated in populate indicators.
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pd.set_option('display.max_rows', None)
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pd.set_option('display.max_columns', None)
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pd.set_option("display.width", 200)
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# print(f"{indicator} {crossed_indicator} {real_num}")
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dataframe[indicator] = gene_calculator(dataframe, indicator)
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dataframe[crossed_indicator] = gene_calculator(dataframe, crossed_indicator)
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indicator_trend_sma = f"{indicator}-SMA-{TREND_CHECK_CANDLES}"
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if operator in ["UT", "DT", "OT", "CUT", "CDT", "COT"]:
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dataframe[indicator_trend_sma] = gene_calculator(dataframe, indicator_trend_sma)
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if operator == ">":
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condition = (dataframe[indicator] > dataframe[crossed_indicator])
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elif operator == "=":
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condition = (np.isclose(dataframe[indicator], dataframe[crossed_indicator]))
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elif operator == "<":
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condition = (dataframe[indicator] < dataframe[crossed_indicator])
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elif operator == "C":
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condition = (
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(qtpylib.crossed_below(dataframe[indicator], dataframe[crossed_indicator])) |
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(qtpylib.crossed_above(
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dataframe[indicator], dataframe[crossed_indicator]))
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)
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elif operator == "CA":
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condition = (qtpylib.crossed_above(dataframe[indicator], dataframe[crossed_indicator]))
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elif operator == "CB":
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condition = (qtpylib.crossed_below(dataframe[indicator], dataframe[crossed_indicator]))
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elif operator == ">R":
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condition = (dataframe[indicator] > real_num)
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elif operator == "=R":
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condition = (np.isclose(dataframe[indicator], real_num))
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elif operator == "<R":
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condition = (dataframe[indicator] < real_num)
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elif operator == "/>R":
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condition = (dataframe[indicator].div(dataframe[crossed_indicator]) > real_num)
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elif operator == "/=R":
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condition = (np.isclose(dataframe[indicator].div(dataframe[crossed_indicator]), real_num))
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elif operator == "/<R":
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condition = (dataframe[indicator].div(dataframe[crossed_indicator]) < real_num)
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elif operator == "UT":
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condition = (dataframe[indicator] > dataframe[indicator_trend_sma])
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elif operator == "DT":
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condition = (dataframe[indicator] < dataframe[indicator_trend_sma])
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elif operator == "OT":
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condition = (np.isclose(dataframe[indicator], dataframe[indicator_trend_sma]))
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elif operator == "CUT":
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condition = (
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(
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qtpylib.crossed_above(dataframe[indicator],dataframe[indicator_trend_sma])
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) & (
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dataframe[indicator] > dataframe[indicator_trend_sma]
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)
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)
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elif operator == "CDT":
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condition = (
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(
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qtpylib.crossed_below(dataframe[indicator], dataframe[indicator_trend_sma])
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) &
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(
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dataframe[indicator] < dataframe[indicator_trend_sma]
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)
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)
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elif operator == "COT":
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condition = (
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(
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(
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qtpylib.crossed_below(dataframe[indicator], dataframe[indicator_trend_sma])
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) |
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(
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qtpylib.crossed_above(dataframe[indicator], dataframe[indicator_trend_sma])
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)
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) &
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(
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np.isclose(dataframe[indicator], dataframe[indicator_trend_sma])
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)
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)
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return condition, dataframe
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# #########################################################################################################################
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# This class is a sample. Feel free to customize it.
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class Empty(IStrategy):
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@@ -364,6 +187,14 @@ class Empty(IStrategy):
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}
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}
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start_bull_indicator = CategoricalParameter(sma_indicators_d, default='sma100', space='buy', optimize=True, load=True)
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start_bull_deriv1 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=True, load=True)
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start_bull_deriv2 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=True, load=True)
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start_bear_indicator = CategoricalParameter(sma_indicators_d, default='sma100', space='buy', optimize=True, load=True)
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start_bear_deriv1 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=True, load=True)
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start_bear_deriv2 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=True, load=True)
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buy_deriv1_sma60 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=False, load=True)
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buy_deriv1_sma5d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
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buy_deriv1_sma12d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
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@@ -372,48 +203,24 @@ class Empty(IStrategy):
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buy_deriv2_sma5d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
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buy_deriv2_sma12d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
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buy_longue = CategoricalParameter(long_timeperiods, default=120, space='buy')
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# Buy Hyperoptable Parameters/Spaces.
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# buy_crossed_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="ADD-20", space='buy')
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# buy_crossed_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="ASIN-6", space='buy')
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# buy_crossed_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLEVENINGSTAR-50", space='buy')
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#
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# buy_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="SMA-100", space='buy')
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# buy_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="WILLR-50", space='buy')
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# buy_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLHANGINGMAN-20", space='buy')
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#
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# buy_operator0 = CategoricalParameter(operators, default="/<R", space='buy')
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# buy_operator1 = CategoricalParameter(operators, default="<R", space='buy')
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# buy_operator2 = CategoricalParameter(operators, default="CB", space='buy')
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#
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# buy_real_num0 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='buy')
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# buy_real_num1 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='buy')
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# buy_real_num2 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='buy')
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# Sell Hyperoptable Parameters/Spaces.
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# sell_crossed_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="CDLSHOOTINGSTAR-150", space='sell')
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# sell_crossed_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="MAMA-1-100", space='sell')
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# sell_crossed_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLMATHOLD-6", space='sell')
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#
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# sell_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="CDLUPSIDEGAP2CROWS-5", space='sell')
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# sell_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="CDLHARAMICROSS-150", space='sell')
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# sell_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDL2CROWS-5", space='sell')
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#
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# sell_operator0 = CategoricalParameter(operators, default="<R", space='sell')
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# sell_operator1 = CategoricalParameter(operators, default="D", space='sell')
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# sell_operator2 = CategoricalParameter(operators, default="/>R", space='sell')
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#
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# sell_real_num0 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='sell')
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# sell_real_num1 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='sell')
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# sell_real_num2 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='sell')
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buy_longue = CategoricalParameter(long_timeperiods, default=120, space='buy', optimize=False, load=True)
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buy_longue_derive = CategoricalParameter(sma_deriv1_indicators, default="sma60_deriv1_1d", space='buy', optimize=False, load=True)
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sell_score_indicator = CategoricalParameter(score_indicators, default="sma24_score", space='sell')
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sell_sma_indicators = CategoricalParameter(sma_indicators, default="sma24_score", space='sell')
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sell_crossed_sma_indicators = CategoricalParameter(sma_indicators, default="sma24_score", space='sell')
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# sell_sma_indicators = CategoricalParameter(sma_indicators, default="sma24_score", space='sell')
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# sell_crossed_sma_indicators = CategoricalParameter(sma_indicators, default="sma24_score", space='sell')
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drop_from_last_entry = DecimalParameter(-0.1, 0, decimals=2, default=-0.025, space='protection', optimize=False, load=True)
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# baisses = list()
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# for i in range(0, 0.5, 0.05):
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# baisses.append(i)
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baisse = DecimalParameter(0, 0.5, decimals=2, default=0.3, space='protection', optimize=True, load=True)
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b30_indicateur = CategoricalParameter(sma_indicators_h, default="sma36", space='protection', optimize=True, load=True)
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# lost_indicator = CategoricalParameter(sma_deriv1_indicators, default="sma5_deriv1", space='protection')
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drop_from_last_entry = DecimalParameter(-0.1, 0, decimals=2, default=-0.025, space='protection')
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# range_pos_stoploss = DecimalParameter(0, 0.1, decimals=2, default=0.05, space='protection')
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# stoploss_force = DecimalParameter(-0.2, 0, decimals=2, default=-0.05, space='protection')
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@@ -438,12 +245,23 @@ class Empty(IStrategy):
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#
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# range_pos = (last_candle['close'] - range_min) / (range_max - range_min)
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range_pos = last_candle[f"range_pos"]
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sl_min = self.wallets.get_available_stake_amount() / 6
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sl_max = self.wallets.get_available_stake_amount() / 2
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# range_pos = last_candle[f"range_pos"]
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# sl_min = self.wallets.get_available_stake_amount() / 6
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# sl_max = self.wallets.get_available_stake_amount() / 2
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#
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# amount = sl_min + (1 - range_pos) * (sl_max - sl_min)
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if last_candle['enter_tag'] == 'fall':
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amount = self.wallets.get_available_stake_amount() / 5
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else:
|
||||
amount = self.wallets.get_available_stake_amount() # / (2 * self.pairs[pair]['count_of_lost'] + 1)
|
||||
|
||||
amount = sl_min + (1 - range_pos) * (sl_max - sl_min)
|
||||
amount = self.wallets.get_available_stake_amount()
|
||||
# factor = 1
|
||||
#
|
||||
# if self.pairs[pair]['last_trade'] is not None \
|
||||
# and self.pairs[pair]['last_trade'].close_profit is not None \
|
||||
# and self.pairs[pair]['last_trade'].close_profit > 0.01:
|
||||
# factor = self.pairs[pair]['last_trade'].close_profit / 0.01
|
||||
# amount = amount / factor
|
||||
|
||||
return min(amount, self.wallets.get_available_stake_amount())
|
||||
|
||||
@@ -546,6 +364,9 @@ class Empty(IStrategy):
|
||||
last_candle_2 = dataframe.iloc[-2].squeeze()
|
||||
last_candle_3 = dataframe.iloc[-3].squeeze()
|
||||
|
||||
# if (last_candle['has_cross_min_6'] and last_candle['sma60_deriv1'] > 0) or (last_candle['sma12_inv_1d']):
|
||||
# self.pairs[pair]['count_of_lost'] = 0
|
||||
|
||||
condition = False if self.pairs[pair]['count_of_lost'] >= 1 \
|
||||
and (last_candle['sma12_deriv1_1d'] < 0.001 \
|
||||
or last_candle['sma12_deriv2_1d'] < 0.001) else True
|
||||
@@ -619,19 +440,19 @@ class Empty(IStrategy):
|
||||
buys=1,
|
||||
stake=round(stake_amount, 2)
|
||||
)
|
||||
else:
|
||||
self.log_trade(
|
||||
last_candle=last_candle,
|
||||
date=current_time,
|
||||
action=("🟩Buy" if allow_to_buy else "Canceled") + " " + reason,
|
||||
pair=pair,
|
||||
rate=rate,
|
||||
dispo=dispo,
|
||||
profit=0,
|
||||
trade_type=entry_tag,
|
||||
buys=1,
|
||||
stake=0
|
||||
)
|
||||
# else:
|
||||
# self.log_trade(
|
||||
# last_candle=last_candle,
|
||||
# date=current_time,
|
||||
# action=("🟩Buy" if allow_to_buy else "Canceled") + " " + reason,
|
||||
# pair=pair,
|
||||
# rate=rate,
|
||||
# dispo=dispo,
|
||||
# profit=0,
|
||||
# trade_type=entry_tag,
|
||||
# buys=1,
|
||||
# stake=0
|
||||
# )
|
||||
return allow_to_buy
|
||||
|
||||
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
|
||||
@@ -701,6 +522,8 @@ class Empty(IStrategy):
|
||||
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
|
||||
max_profit = self.pairs[pair]['max_profit']
|
||||
|
||||
# baisse_min = last_candle['close'] - last_candle['min12']
|
||||
|
||||
baisse = 0
|
||||
if profit > 0:
|
||||
baisse = 1 - (profit / max_profit)
|
||||
@@ -722,19 +545,19 @@ class Empty(IStrategy):
|
||||
# buys=count_of_buys,
|
||||
# stake=0
|
||||
# )
|
||||
# if self.pairs[pair]['current_trade'].enter_tag == 'fall':
|
||||
# if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
|
||||
# self.pairs[pair]['force_sell'] = True
|
||||
# return 'sma60'
|
||||
# else:
|
||||
|
||||
if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60']:
|
||||
self.pairs[pair]['force_sell'] = True
|
||||
return 'sma60'
|
||||
|
||||
cross = qtpylib.crossed_below(dataframe[self.sell_sma_indicators.value], dataframe[self.sell_crossed_sma_indicators.value])
|
||||
|
||||
# if profit > 0 and cross.iloc[-1]:
|
||||
# self.pairs[pair]['force_sell'] = True
|
||||
# return 'Cross'
|
||||
|
||||
if profit > max(5, expected_profit) and baisse > 0.30 and last_candle[f"close"] <= last_candle['sma36']:
|
||||
# and last_candle['percent3'] < 0 and last_candle['percent5'] < 0:
|
||||
if profit > max(5, expected_profit) and \
|
||||
(baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
|
||||
and last_candle['hapercent'] <0 :
|
||||
self.pairs[pair]['force_sell'] = True
|
||||
return 'B30'
|
||||
|
||||
@@ -827,7 +650,9 @@ class Empty(IStrategy):
|
||||
dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
|
||||
dataframe['zero'] = 0
|
||||
|
||||
dataframe[f"percent"] = dataframe['close'].pct_change()
|
||||
for timeperiod in timeperiods:
|
||||
dataframe[f"mid_smooth{timeperiod}"] = dataframe['mid'].rolling(timeperiod).mean()
|
||||
dataframe[f'max{timeperiod}'] = talib.MAX(dataframe['close'], timeperiod=timeperiod)
|
||||
dataframe[f'min{timeperiod}'] = talib.MIN(dataframe['close'], timeperiod=timeperiod)
|
||||
dataframe[f"percent{timeperiod}"] = dataframe['close'].pct_change(timeperiod)
|
||||
@@ -853,11 +678,27 @@ class Empty(IStrategy):
|
||||
|
||||
for timeperiod in long_timeperiods:
|
||||
informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
|
||||
self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
|
||||
|
||||
informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
|
||||
self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
|
||||
informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
|
||||
informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
|
||||
|
||||
informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
|
||||
informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
|
||||
|
||||
latched = np.zeros(len(informative), dtype=bool)
|
||||
|
||||
for i in range(1, len(informative)):
|
||||
if informative['stop_buying_deb'].iloc[i]:
|
||||
latched[i] = True
|
||||
elif informative['stop_buying_end'].iloc[i]:
|
||||
latched[i] = False
|
||||
else:
|
||||
latched[i] = latched[i - 1]
|
||||
informative['stop_buying'] = latched
|
||||
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
||||
# ######################################################################################################
|
||||
|
||||
@@ -868,7 +709,8 @@ class Empty(IStrategy):
|
||||
|
||||
# dataframe['cross_sma60'] = qtpylib.crossed_below(dataframe[self.sell_sma_indicators.value], dataframe[self.sell_crossed_sma_indicators.value])
|
||||
|
||||
dataframe[f'stop_buying'] = qtpylib.crossed_below(dataframe[f"sma12"], dataframe['sma48'])
|
||||
dataframe[f'has_cross_min'] = qtpylib.crossed_above(dataframe[f"close"], dataframe['min60_1d'])
|
||||
dataframe[f'has_cross_min_6'] = (dataframe['has_cross_min'].rolling(6).max() == 1)
|
||||
|
||||
dataframe['atr'] = talib.ATR(dataframe)
|
||||
dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
|
||||
@@ -891,62 +733,13 @@ class Empty(IStrategy):
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# (dataframe['sma24_score'].shift(2) <= dataframe['zero'])
|
||||
# & (dataframe['sma5'].shift(1) <= dataframe['sma5'])
|
||||
# & (dataframe['sma5_inv'] == -1)
|
||||
# & (dataframe['min24'].shift(3) == dataframe['min24'])
|
||||
# ),
|
||||
# 'buy'] = 1
|
||||
|
||||
conditions = list()
|
||||
|
||||
# # print(dataframe.columns)
|
||||
#
|
||||
# buy_indicator = self.buy_indicator0.value
|
||||
# buy_crossed_indicator = self.buy_crossed_indicator0.value
|
||||
# buy_operator = self.buy_operator0.value
|
||||
# buy_real_num = self.buy_real_num0.value
|
||||
# condition, dataframe = condition_generator(
|
||||
# dataframe,
|
||||
# buy_operator,
|
||||
# buy_indicator,
|
||||
# buy_crossed_indicator,
|
||||
# buy_real_num
|
||||
# )
|
||||
# conditions.append(condition)
|
||||
# # backup
|
||||
# buy_indicator = self.buy_indicator1.value
|
||||
# buy_crossed_indicator = self.buy_crossed_indicator1.value
|
||||
# buy_operator = self.buy_operator1.value
|
||||
# buy_real_num = self.buy_real_num1.value
|
||||
#
|
||||
# condition, dataframe = condition_generator(
|
||||
# dataframe,
|
||||
# buy_operator,
|
||||
# buy_indicator,
|
||||
# buy_crossed_indicator,
|
||||
# buy_real_num
|
||||
# )
|
||||
# conditions.append(condition)
|
||||
#
|
||||
# buy_indicator = self.buy_indicator2.value
|
||||
# buy_crossed_indicator = self.buy_crossed_indicator2.value
|
||||
# buy_operator = self.buy_operator2.value
|
||||
# buy_real_num = self.buy_real_num2.value
|
||||
# condition, dataframe = condition_generator(
|
||||
# dataframe,
|
||||
# buy_operator,
|
||||
# buy_indicator,
|
||||
# buy_crossed_indicator,
|
||||
# buy_real_num
|
||||
# )
|
||||
# conditions.append(condition)
|
||||
# conditions.append((dataframe[self.stop_buying_indicator.value] == False) | (dataframe['range_pos'] < 0))
|
||||
|
||||
#conditions.append((dataframe[self.stop_buying_indicator.value] == False) | (dataframe['range_pos'] < 0))
|
||||
|
||||
# #####################################################################################
|
||||
# CA MONTE !!
|
||||
# #####################################################################################
|
||||
# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv1_1d" ] > self.start_bull_deriv1.value)
|
||||
# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv2_1d"] > self.start_bull_deriv2.value)
|
||||
conditions.append(dataframe['sma12_deriv1'] > self.buy_deriv1_sma60.value)
|
||||
conditions.append(dataframe['sma5_deriv1_1d'] > self.buy_deriv1_sma5d.value)
|
||||
conditions.append(dataframe['sma12_deriv1_1d'] > self.buy_deriv1_sma12d.value)
|
||||
@@ -962,11 +755,6 @@ class Empty(IStrategy):
|
||||
|
||||
dynamic_rsi_threshold = 70 + 15 * np.tanh(dataframe["dist_sma200_1d"] * 5)
|
||||
conditions.append((dataframe['max_rsi_12_1d'] < dynamic_rsi_threshold))
|
||||
# | (
|
||||
# (dataframe['sma5_deriv1'] > 0) & (dataframe['sma12_deriv1'] > 0) & (dataframe['sma24_deriv1'] > 0) & (
|
||||
# dataframe['sma48_deriv1'] > 0) & (dataframe['sma60_deriv1'] > 0) & (dataframe['sma5_deriv1_1d'] > 0))
|
||||
# )
|
||||
|
||||
conditions.append(dataframe[f"close"] > dataframe['sma60'])
|
||||
conditions.append(((dataframe[f"range_pos"] < 0.05) ) | ((dataframe['sma12_deriv1'] > 0) & (dataframe['sma12_deriv2'] > 0)))
|
||||
|
||||
@@ -975,17 +763,84 @@ class Empty(IStrategy):
|
||||
| (dataframe['sma60_inv_1d'] == -1)
|
||||
)
|
||||
|
||||
# print(f"BUY indicators tested \n"
|
||||
# f"{self.buy_indicator0.value} {self.buy_crossed_indicator0.value} {self.buy_operator0.value} {self.buy_real_num0.value} \n"
|
||||
# f"{self.buy_indicator1.value} {self.buy_crossed_indicator1.value} {self.buy_operator1.value} {self.buy_real_num1.value} \n"
|
||||
# f"{self.buy_indicator2.value} {self.buy_crossed_indicator2.value} {self.buy_operator2.value} {self.buy_real_num2.value} \n"
|
||||
# )
|
||||
|
||||
if conditions:
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
['enter_long', 'enter_tag']
|
||||
] = (1, 'god')
|
||||
] = (1, 'bull')
|
||||
|
||||
# #####################################################################################
|
||||
# CA BAISSE !!
|
||||
# "buy": {
|
||||
# "buy_deriv1_sma12d": 0.0,
|
||||
# "buy_deriv1_sma5d": 0.0,
|
||||
# "buy_deriv1_sma60": 0.003,
|
||||
# "buy_deriv2_sma12d": -0.03,
|
||||
# "buy_deriv2_sma5d": 0.0,
|
||||
# "buy_deriv2_sma60": -0.002,
|
||||
# "buy_longue": 200,
|
||||
# "buy_longue_derive": "sma160_deriv1_1d"
|
||||
# },
|
||||
# "protection": {
|
||||
# "drop_from_last_entry": -0.03,
|
||||
# "b30_indicateur": "sma3",
|
||||
# "baisse": 0.31
|
||||
# },
|
||||
# #####################################################################################
|
||||
conditions = list()
|
||||
conditions.append(dataframe[f"{self.start_bear_indicator.value}_deriv1_1d" ] < self.start_bear_deriv1.value)
|
||||
conditions.append(dataframe[f"{self.start_bear_indicator.value}_deriv2_1d"] < self.start_bear_deriv2.value)
|
||||
|
||||
conditions.append(dataframe['sma12_deriv1'] > 0.003)
|
||||
conditions.append(dataframe['sma5_deriv1_1d'] > 0.0)
|
||||
conditions.append(dataframe['sma12_deriv1_1d'] > 0.0)
|
||||
|
||||
conditions.append(dataframe['sma12_deriv2'] > -0.002)
|
||||
conditions.append(dataframe['sma5_deriv2_1d'] > 0)
|
||||
conditions.append(dataframe['sma12_deriv2_1d'] > -0.03)
|
||||
|
||||
conditions.append(dataframe['hapercent'] > 0)
|
||||
# conditions.append(dataframe['percent12'] < 0.01)
|
||||
# conditions.append(dataframe['percent5'] < 0.01)
|
||||
conditions.append(dataframe['max_rsi_24'] < 80)
|
||||
|
||||
dynamic_rsi_threshold = 70 + 15 * np.tanh(dataframe["dist_sma200_1d"] * 5)
|
||||
conditions.append((dataframe['max_rsi_12_1d'] < dynamic_rsi_threshold))
|
||||
conditions.append(dataframe[f"close"] > dataframe['sma60'])
|
||||
conditions.append(((dataframe[f"range_pos"] < 0.05) ) | ((dataframe['sma12_deriv1'] > 0) & (dataframe['sma12_deriv2'] > 0)))
|
||||
|
||||
conditions.append(
|
||||
(dataframe['close_1d'] > dataframe[f'sma{self.buy_longue.value}_1d'])
|
||||
| (dataframe['sma60_inv_1d'] == -1)
|
||||
)
|
||||
|
||||
# if conditions:
|
||||
# dataframe.loc[
|
||||
# reduce(lambda x, y: x & y, conditions),
|
||||
# ['enter_long', 'enter_tag']
|
||||
# ] = (1, 'bear')
|
||||
|
||||
# conditions = list()
|
||||
# conditions.append(dataframe['close_1d'] < dataframe[f'sma{self.buy_longue.value}_1d'])
|
||||
# conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
|
||||
# conditions.append(dataframe['mid_smooth5'] > dataframe['mid_smooth5'].shift(1))
|
||||
# conditions.append(dataframe['hapercent'] > 0)
|
||||
# conditions.append(dataframe['percent5'] < 0.01)
|
||||
# dataframe.loc[
|
||||
# reduce(lambda x, y: x & y, conditions),
|
||||
# ['enter_long', 'enter_tag']
|
||||
# ] = (1, 'cross_min')
|
||||
|
||||
# conditions = list()
|
||||
# # conditions.append(dataframe['close_1d'] < dataframe[f'sma{self.buy_longue.value}_1d'])
|
||||
# # conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
|
||||
# # conditions.append(dataframe['mid_smooth5'] > dataframe['mid_smooth5'].shift(1))
|
||||
# conditions.append(dataframe['min12'] == dataframe['min12'].shift(3))
|
||||
# conditions.append((dataframe['percent24'] < -0.025) | (dataframe['percent12'] < -0.025))
|
||||
# dataframe.loc[
|
||||
# reduce(lambda x, y: x & y, conditions),
|
||||
# ['enter_long', 'enter_tag']
|
||||
# ] = (1, 'fall')
|
||||
|
||||
return dataframe
|
||||
|
||||
@@ -1087,35 +942,35 @@ class Empty(IStrategy):
|
||||
|
||||
# ####################################################################
|
||||
# Calcul de la pente lissée
|
||||
d1 = series.diff()
|
||||
d1_smooth = d1.rolling(5).mean()
|
||||
# Normalisation
|
||||
z = (d1_smooth - d1_smooth.rolling(ema_period).mean()) / d1_smooth.rolling(ema_period).std()
|
||||
# d1 = series.diff()
|
||||
# d1_smooth = d1.rolling(5).mean()
|
||||
# # Normalisation
|
||||
# z = (d1_smooth - d1_smooth.rolling(ema_period).mean()) / d1_smooth.rolling(ema_period).std()
|
||||
|
||||
dataframe[f"{name}{suffixe}_trend_up"] = (
|
||||
(d1_smooth.shift(1) < 0) &
|
||||
(d1_smooth > 0) &
|
||||
(z > 1.0)
|
||||
)
|
||||
|
||||
dataframe[f"{name}{suffixe}_trend_down"] = (
|
||||
(d1_smooth.shift(1) > 0) &
|
||||
(d1_smooth < 0) &
|
||||
(z < -1.0)
|
||||
)
|
||||
|
||||
momentum_short = d1.rolling(int(ema_period / 2)).mean()
|
||||
momentum_long = d1.rolling(ema_period * 2).mean()
|
||||
|
||||
dataframe[f"{name}{suffixe}_trend_change_up"] = (
|
||||
(momentum_short.shift(1) < momentum_long.shift(1)) &
|
||||
(momentum_short > momentum_long)
|
||||
)
|
||||
|
||||
dataframe[f"{name}{suffixe}_trend_change_down"] = (
|
||||
(momentum_short.shift(1) > momentum_long.shift(1)) &
|
||||
(momentum_short < momentum_long)
|
||||
)
|
||||
# dataframe[f"{name}{suffixe}_trend_up"] = (
|
||||
# (d1_smooth.shift(1) < 0) &
|
||||
# (d1_smooth > 0) &
|
||||
# (z > 1.0)
|
||||
# )
|
||||
#
|
||||
# dataframe[f"{name}{suffixe}_trend_down"] = (
|
||||
# (d1_smooth.shift(1) > 0) &
|
||||
# (d1_smooth < 0) &
|
||||
# (z < -1.0)
|
||||
# )
|
||||
#
|
||||
# momentum_short = d1.rolling(int(ema_period / 2)).mean()
|
||||
# momentum_long = d1.rolling(ema_period * 2).mean()
|
||||
#
|
||||
# dataframe[f"{name}{suffixe}_trend_change_up"] = (
|
||||
# (momentum_short.shift(1) < momentum_long.shift(1)) &
|
||||
# (momentum_short > momentum_long)
|
||||
# )
|
||||
#
|
||||
# dataframe[f"{name}{suffixe}_trend_change_down"] = (
|
||||
# (momentum_short.shift(1) > momentum_long.shift(1)) &
|
||||
# (momentum_short < momentum_long)
|
||||
# )
|
||||
|
||||
return dataframe
|
||||
|
||||
@@ -1290,4 +1145,12 @@ class Empty(IStrategy):
|
||||
expected_profit = lim * self.pairs[pair]['total_amount'] # min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
|
||||
|
||||
self.pairs[pair]['expected_profit'] = expected_profit
|
||||
return expected_profit
|
||||
return expected_profit
|
||||
|
||||
def getLastClosedTrade(self):
|
||||
# récupérer le dernier trade fermé
|
||||
trades = Trade.get_trades_proxy(pair=pair,is_open=False)
|
||||
if trades:
|
||||
last_trade = trades[-1]
|
||||
self.pairs[pair]['last_profit'] = last_trade.close_profit # ex: 0.12 = +12%
|
||||
self.pairs[pair]['last_trade'] = last_trade
|
||||
Reference in New Issue
Block a user