import logging from functools import reduce import pandas as pd import talib.abstract as ta from pandas import DataFrame from technical import qtpylib from freqtrade.exchange import timeframe_to_prev_date from freqtrade.freqai.strategy_bridge import CustomModel from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair from freqtrade.strategy.interface import IStrategy logger = logging.getLogger(__name__) class Cenderawasih_freqai(IStrategy): """ Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.model = CustomModel(self.config) self.model.bridge.start(dataframe, metadata) to make predictions on their data. populate_any_indicators() automatically generates the variety of features indicated by the user in the canonical freqtrade configuration file under config['freqai']. """ minimal_roi = {"0": 0.1, "240": -1} plot_config = { "main_plot": {}, "subplots": { "prediction": {"prediction": {"color": "blue"}}, "target_roi": { "target_roi": {"color": "brown"}, }, "do_predict": { "do_predict": {"color": "brown"}, }, }, } process_only_new_candles = True stoploss = -0.05 use_exit_signal = True startup_candle_count: int = 300 can_short = False def informative_pairs(self): whitelist_pairs = self.dp.current_whitelist() corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"] informative_pairs = [] for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]: for pair in whitelist_pairs: informative_pairs.append((pair, tf)) for pair in corr_pairs: if pair in whitelist_pairs: continue # avoid duplication informative_pairs.append((pair, tf)) return informative_pairs def bot_start(self): self.model = CustomModel(self.config) def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): """ Function designed to automatically generate, name and merge features from user indicated timeframes in the configuration file. User controls the indicators passed to the training/prediction by prepending indicators with `'%-' + coin ` (see convention below). I.e. user should not prepend any supporting metrics (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the model. :params: :pair: pair to be used as informative :df: strategy dataframe which will receive merges from informatives :tf: timeframe of the dataframe which will modify the feature names :informative: the dataframe associated with the informative pair :coin: the name of the coin which will modify the feature names. """ with self.model.bridge.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: t = int(t) informative[f"{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) informative[f"{coin}hma-period_{t}"] = tv_hma(informative, timeperiod=t) informative[f"%-{coin}close_below_ema-period_{t}"] = ( informative["close"] / informative[f"{coin}ema-period_{t}"] ) informative[f"%-{coin}close_below_hma-period_{t}"] = ( informative["close"] / informative[f"{coin}hma-period_{t}"] ) informative[f"%-{coin}rsi-period_14"] = ta.RSI(informative, timeperiod=14) informative[f"%-{coin}rsi-period_4"] = ta.RSI(informative, timeperiod=4) informative[f"%-{coin}pct-change"] = informative["close"].pct_change() informative[f"%-{coin}raw_volume"] = informative["volume"] informative[f"%-{coin}raw_price"] = informative["close"] indicators = [col for col in informative if col.startswith("%")] 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) if set_generalized_indicators: df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 df["&-s_close"] = ( df["close"] .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) .mean() / df["close"] - 1 ) return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] self.pair = metadata["pair"] sgi = True for tf in self.freqai_info["feature_parameters"]["include_timeframes"]: dataframe = self.populate_any_indicators( metadata, self.pair, dataframe.copy(), tf, coin=self.pair.split("/")[0] + "-", set_generalized_indicators=sgi, ) sgi = False for pair in self.freqai_info["feature_parameters"]["include_corr_pairlist"]: if metadata["pair"] in pair: continue # do not include whitelisted pair twice if it is in corr_pairlist dataframe = self.populate_any_indicators( metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-" ) dataframe = self.model.bridge.start(dataframe, metadata, self) dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]] if enter_long_conditions: df.loc[ reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25] if exit_long_conditions: df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 return df def get_ticker_indicator(self): return int(self.config["timeframe"][:-1]) def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time, **kwargs,) -> bool: entry_tag = trade.enter_tag follow_mode = self.config.get("freqai", {}).get("follow_mode", False) if not follow_mode: pair_dict = self.model.bridge.dd.pair_dict else: pair_dict = self.model.bridge.dd.follower_dict with self.model.bridge.lock: pair_dict[pair]["prediction" + entry_tag] = 0 if not follow_mode: self.model.bridge.dd.save_drawer_to_disk() else: self.model.bridge.dd.save_follower_dict_to_disk() return True def tv_wma(df, length = 9) -> DataFrame: """ Source: Tradingview "Moving Average Weighted" Pinescript Author: Unknown Args : dataframe : Pandas Dataframe length : WMA length field : Field to use for the calculation Returns : dataframe : Pandas DataFrame with new columns 'tv_wma' """ norm = 0 sum = 0 for i in range(1, length - 1): weight = (length - i) * length norm = norm + weight sum = sum + df.shift(i) * weight tv_wma = (sum / norm) if norm > 0 else 0 return tv_wma def tv_hma(dataframe, length = 9, field = 'close') -> DataFrame: """ Source: Tradingview "Hull Moving Average" Pinescript Author: Unknown Args : dataframe : Pandas Dataframe length : HMA length field : Field to use for the calculation Returns : dataframe : Pandas DataFrame with new columns 'tv_hma' """ h = 2 * tv_wma(dataframe[field], math.floor(length / 2)) - tv_wma(dataframe[field], length) tv_hma = tv_wma(h, math.floor(math.sqrt(length))) return tv_hma