# source: https://raw.githubusercontent.com/Filimonov-Eremeyka/freqtrade/edf4ff8f6dc4d6617f318dd5ebaa0c3427654b1f/user_data/strategies/FinalZigZagStrategy.py # user_data/strategies/Github_Filimonov_Eremeyka_freqtrade__FinalZigZagStrategy__20250905_154014.py import numpy as np import pandas as pd from pandas import DataFrame from freqtrade.strategy import IStrategy import logging # Импортируем нашу реализацию ZigZag from zigzag_indicator import zigzag class Github_Filimonov_Eremeyka_freqtrade__FinalZigZagStrategy__20250905_154014(IStrategy): """ ФИНАЛЬНАЯ СТРАТЕГИЯ ВЕРСИЯ 4.0 (с исправлениями от эксперта): - Понижен порог ZigZag для 5m таймфрейма. - Исправлена ошибка TypeError при логировании. - Улучшена работа с DataFrame для предотвращения проблем с типами данных. """ INTERFACE_VERSION = 3 timeframe = '5m' startup_candle_count = 200 # <-- Возвращаем стандартное значение, т.к. таймрейндж достаточный logger = logging.getLogger(__name__) minimal_roi = {"0": 100} stoploss = -1.0 # --- ИЗМЕНЕННЫЕ ПАРАМЕТРЫ для теста --- zigzag_percent: float = 0.001 # 0.5% - более адекватный порог для 5m level_len: int = 60 # Время жизни уровня max_slots: int = 4 # Максимум параллельных уровней min_dist_pct: float = 0.0001 # 0.1% - более мягкий фильтр для близких уровней # --- plot_config с plotly-синтаксисом --- plot_config = { 'main_plot': { 'zigzag_peak': { 'type': 'scatter', 'plotly': {'mode': 'markers', 'marker': {'symbol': 'diamond', 'size': 9}} }, 'zigzag_trough': { 'type': 'scatter', 'plotly': {'mode': 'markers', 'marker': {'symbol': 'diamond', 'size': 9}} }, 'zigzag_high_0': {'type': 'scatter', 'plotly': {'mode': 'lines'}}, 'zigzag_high_1': {'type': 'scatter', 'plotly': {'mode': 'lines'}}, 'zigzag_high_2': {'type': 'scatter', 'plotly': {'mode': 'lines'}}, 'zigzag_high_3': {'type': 'scatter', 'plotly': {'mode': 'lines'}}, 'zigzag_low_0': {'type': 'scatter', 'plotly': {'mode': 'lines'}}, 'zigzag_low_1': {'type': 'scatter', 'plotly': {'mode': 'lines'}}, 'zigzag_low_2': {'type': 'scatter', 'plotly': {'mode': 'lines'}}, 'zigzag_low_3': {'type': 'scatter', 'plotly': {'mode': 'lines'}}, } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 1) ZigZag peaks, troughs = zigzag(dataframe, peak_pct=self.zigzag_percent, trough_pct=self.zigzag_percent) dataframe['zigzag_peak'] = peaks dataframe['zigzag_trough'] = troughs # --- Логи по ZigZag --- peak_cnt = int(pd.notna(dataframe['zigzag_peak']).sum()) trough_cnt = int(pd.notna(dataframe['zigzag_trough']).sum()) self.logger.info(f"[ZZ] peaks={peak_cnt}, troughs={trough_cnt}, timeframe={self.timeframe}") if peak_cnt == 0 and trough_cnt == 0: self.logger.warning(f"[ZZ] No peaks/troughs. Try lower zigzag_percent (now {self.zigzag_percent}).") if peak_cnt > 0: peak_idx = list(np.where(pd.notna(dataframe['zigzag_peak']))[0]) self.logger.info(f"[ZZ] first/last peak idx: {peak_idx[0]} / {peak_idx[-1]}") if trough_cnt > 0: trough_idx = list(np.where(pd.notna(dataframe['zigzag_trough']))[0]) self.logger.info(f"[ZZ] first/last trough idx: {trough_idx[0]} / {trough_idx[-1]}") # 2) Уровни dataframe = self._build_timeboxed_levels( df=dataframe, point_high_col='zigzag_peak', point_low_col='zigzag_trough' ) # --- ИСПРАВЛЕННЫЕ ЛОГИ по уровням --- level_cols = [c for c in dataframe.columns if c.startswith('zigzag_high_') or c.startswith('zigzag_low_')] nonempty_levels = {c: int(pd.notna(dataframe[c]).sum()) for c in level_cols} total_points = sum(nonempty_levels.values()) self.logger.info(f"[LVL] total level-points={total_points} per column={nonempty_levels}") if total_points == 0 and (peak_cnt > 0 or trough_cnt > 0): self.logger.warning("[LVL] All level columns are empty despite ZZ points. Check logic.") return dataframe def _build_timeboxed_levels(self, df: DataFrame, point_high_col: str, point_low_col: str) -> DataFrame: # ИСПРАВЛЕНИЕ: работаем с копией и сбрасываем индекс, чтобы не менять оригинал df_processed = df.copy() df_processed.reset_index(drop=True, inplace=True) for s in range(self.max_slots): df_processed[f'zigzag_high_{s}'] = np.nan df_processed[f'zigzag_low_{s}'] = np.nan active_high = {} active_low = {} created_high = 0 created_low = 0 def _far_enough(active_dict, price) -> bool: for v in active_dict.values(): if v['price'] == 0: continue if abs(price - v['price']) / v['price'] < self.min_dist_pct: return False return True def _place_new_level(active_dict, price, kind: str): nonlocal created_high, created_low for s in range(self.max_slots): if s not in active_dict: active_dict[s] = {'price': float(price), 'ttl': self.level_len} if kind == 'H': created_high += 1 else: created_low += 1 return oldest_slot = min(active_dict, key=lambda k: active_dict[k]['ttl']) active_dict[oldest_slot] = {'price': float(price), 'ttl': self.level_len} if kind == 'H': created_high += 1 else: created_low += 1 high_col_idx = df_processed.columns.get_loc(point_high_col) low_col_idx = df_processed.columns.get_loc(point_low_col) for i in range(len(df_processed)): for s, v in list(active_high.items()): df_processed.iat[i, df_processed.columns.get_loc(f'zigzag_high_{s}')] = v['price'] v['ttl'] -= 1 if v['ttl'] <= 0: del active_high[s] for s, v in list(active_low.items()): df_processed.iat[i, df_processed.columns.get_loc(f'zigzag_low_{s}')] = v['price'] v['ttl'] -= 1 if v['ttl'] <= 0: del active_low[s] peak_price = df_processed.iat[i, high_col_idx] if not np.isnan(peak_price) and _far_enough(active_high, peak_price): _place_new_level(active_high, peak_price, 'H') trough_price = df_processed.iat[i, low_col_idx] if not np.isnan(trough_price) and _far_enough(active_low, trough_price): _place_new_level(active_low, trough_price, 'L') self.logger.info(f"[LVL] created_high={created_high}, created_low={created_low}") # ИСПРАВЛЕНИЕ: Копируем только нужные колонки обратно в исходный df level_cols = [c for c in df_processed.columns if c.startswith('zigzag_high_') or c.startswith('zigzag_low_')] df[level_cols] = df_processed[level_cols] return df # Пустые методы def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['enter_long'] = 0 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['exit_long'] = 0 return dataframe