Start to integration strategy
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Porter
@@ -14,7 +14,7 @@ from technical.util import resample_to_interval, resampled_merge
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import talib.abstract as ta
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from pandas import DataFrame
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade
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from freqtrade.persistence import Trade, Order
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from typing import Optional
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import logging
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logger = logging.getLogger(__name__)
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@@ -42,14 +42,14 @@ class ChanLun_BTC_30(IStrategy):
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"1200": 0
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}
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# 5m and 15m
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minimal_roi_1 = {
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minimal_roi = {
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"0": 0.1,
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"60": 0.05,
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"120": 0.02,
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"240": 0
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}
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# 15m and 30m
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minimal_roi_1 = {
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minimal_roi = {
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"0": 0.1,
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"240": 0.05,
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"480": 0.03,
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@@ -62,12 +62,12 @@ class ChanLun_BTC_30(IStrategy):
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"3600": 0
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}
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can_short = True
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lev = 1.0
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lev = 2.0
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stoploss = -0.3
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trailing_stop = False
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trailing_stop = True
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trailing_stop_positive = 0.025
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trailing_stop_positive_offset = 0.045
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trailing_only_offset_is_reached = False
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trailing_only_offset_is_reached = True
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position_adjustment_enable = True
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startup_candle_count = 600
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@@ -81,6 +81,7 @@ class ChanLun_BTC_30(IStrategy):
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chan = ChanLun()
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chanpy = ChanPY()
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classifier = ChanLunClassifier(None)
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last_trade = None
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# resample our dataframes
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@@ -104,17 +105,13 @@ class ChanLun_BTC_30(IStrategy):
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dataframe_4h = self.add_indicators(dataframe_4h)
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dataframe_1d = self.add_indicators(dataframe_1d)
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#self.chan.plot_dual(dataframe_5, dataframe_30)
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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dataframe_5['chanpy_state'] = self.chanpy.get_bsp_state(dataframe_5)
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state_list, fx_list = self.chan.get_klc_strength_list(dataframe_30)
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dataframe_30['state'] = state_list
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dataframe_30['fx'] = fx_list
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klc_list = self.chan.get_klc_list(dataframe_30)
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bi_list = self.chan.cal_bi_list(klc_list)
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if self.last_time + timedelta(minutes=1) < datetime.now():
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print(state_list[-1], state_list[-2], state_list[-3], state_list[-4], state_list[-5])
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print(fx_list[-1], fx_list[-2], fx_list[-3], fx_list[-4], fx_list[-5])
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print(klc_list[-1].klc_fx_type, klc_list[-2].klc_fx_type, klc_list[-3].klc_fx_type, klc_list[-4].klc_fx_type, klc_list[-5].klc_fx_type)
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print("-------------------------------------------------------------------------------")
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self.last_time = datetime.now()
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dataframe = resampled_merge(dataframe, dataframe_5)
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@@ -168,18 +165,84 @@ class ChanLun_BTC_30(IStrategy):
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else:
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new_exitprice = proposed_rate - 50
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return new_exitprice
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, entry_tag: str | None,
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side: str, **kwargs) -> bool:
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if self.last_trade:
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if self.last_trade.open_date + timedelta(minutes=30) > current_time:
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return False
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#if self.last_trade:
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#print(self.last_trade.open_date, current_time, self.last_trade.open_date + timedelta(minutes=self.time5))
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return True
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def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
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current_profit: float, **kwargs):
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#dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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#last_candle = dataframe.iloc[-1].squeeze()
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"""
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# Above 20% profit, sell when rsi < 80
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if current_profit > 0.2:
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if last_candle["rsi"] < 80:
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return "rsi_below_80"
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# Between 2% and 10%, sell if EMA-long above EMA-short
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if 0.02 < current_profit < 0.1:
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if last_candle["emalong"] > last_candle["emashort"]:
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return "ema_long_below_80"
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# Sell any positions at a loss if they are held for more than one day.
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if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
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return "unclog"
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"""
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if trade.is_short:
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last_high = trade.get_custom_data(key="entry_candle_high")
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if current_rate > last_high:
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print(trade.open_date, last_high, current_rate, "Relay Top FX exit")
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return "Relay Top FX exit"
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else:
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last_low = trade.get_custom_data(key="entry_candle_low")
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if current_rate < last_low:
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print(trade.open_date, last_low, current_rate, "Relay Bottom FX exit")
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return "Relay Bottom FX exit"
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def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
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"""
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Called right after an order fills.
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Will be called for all order types (entry, exit, stoploss, position adjustment).
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:param pair: Pair for trade
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:param trade: trade object.
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:param order: Order object.
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:param current_time: datetime object, containing the current datetime
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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"""
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# Obtain pair dataframe (just to show how to access it)
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dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
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#last_candle = dataframe.iloc[-1].squeeze()
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klc_list = self.chan.get_klc_list(resample_to_interval(dataframe, self.get_ticker_indicator() * 30))
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bi_list = self.chan.cal_bi_list(klc_list)
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last_high = klc_list[-3].high
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last_low = klc_list[-3].low
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if trade.is_short:
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if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
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trade.set_custom_data(key="entry_candle_high", value=last_high)
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else:
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if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
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trade.set_custom_data(key="entry_candle_low", value=last_low)
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print(trade.open_date, trade.close_date, last_high, last_low, order.ft_order_side, klc_list[-2].start_time, klc_list[-2].end_time)
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self.last_trade = trade
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return None
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time30)
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fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time30)
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chanpy_state_str = 'resample_{}_chanpy_state'.format(self.get_ticker_indicator()*self.time5)
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shift_time = self.time30
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shift_time = self.time30*2
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strength = 2.2
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dataframe.loc[
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(
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#(dataframe['state'] == "-30")
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(dataframe[state_str].shift(shift_time) > 1.0) &
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(dataframe[state_str].shift(shift_time) > strength) &
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(dataframe[fx_str].shift(shift_time) == -1) &
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(dataframe[chanpy_state_str].shift(shift_time) == 1)
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(dataframe[chanpy_state_str].shift(shift_time+10) == 1)
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
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@@ -189,9 +252,9 @@ class ChanLun_BTC_30(IStrategy):
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dataframe.loc[
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(
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#(dataframe['state'] == "-30")
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(dataframe[state_str].shift(shift_time) > 1.0) &
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(dataframe[state_str].shift(shift_time) > strength) &
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(dataframe[fx_str].shift(shift_time) == 1) &
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(dataframe[chanpy_state_str].shift(shift_time) == -1)
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(dataframe[chanpy_state_str].shift(shift_time+10) == -1)
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
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@@ -203,13 +266,14 @@ class ChanLun_BTC_30(IStrategy):
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state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time30)
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fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time30)
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chanpy_state_str = 'resample_{}_chanpy_state'.format(self.get_ticker_indicator()*self.time5)
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shift_time = self.time30
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shift_time = self.time30*2
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strength = 2.2
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dataframe.loc[
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(
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#(dataframe['state']== "30")
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(dataframe[state_str].shift(shift_time) > 1.0) &
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(dataframe[state_str].shift(shift_time) > strength) &
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(dataframe[fx_str].shift(shift_time) == 1) &
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(dataframe[chanpy_state_str].shift(shift_time) == -1)
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(dataframe[chanpy_state_str].shift(shift_time+10) == -1)
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
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),
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@@ -217,9 +281,9 @@ class ChanLun_BTC_30(IStrategy):
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dataframe.loc[
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(
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#(dataframe['state']== "30")
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(dataframe[state_str].shift(shift_time) > 1.0) &
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(dataframe[state_str].shift(shift_time) > strength) &
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(dataframe[fx_str].shift(shift_time) == -1) &
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(dataframe[chanpy_state_str].shift(shift_time) == 1)
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(dataframe[chanpy_state_str].shift(shift_time+10) == 1)
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
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#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
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),
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