UbuntuandCursor
206b27fe72
chanlun/indicators/ta.py 接口兼容 talib.abstract，实现代码实际用到的 SMA/MA/EMA/RSI/ATR/MACD/BBANDS；chanlun/pipeline/resample.py 替代 technical.util.resample_to_interval。调用点只改 import，逻辑未动。 暖机长度与平滑种子按 TA-Lib 的约定实现，差一根 K 线就会让下游所有 笔/线段/中枢整体位移。其中 MACD 需特别处理：TA-Lib 让快慢两条 EMA 在同一根 K 线出首值，因而快线的种子取 x[slow-fast:slow] 的均值，而非 从 fastperiod-1 一路递推——两者在百元价位上相差约 0.17。 BBANDS 是有意的分歧：TA-Lib 用 sumsq/n - mean² 求方差，短窗口远离零 时灾难性抵消（timeperiod=2 误差 8.7e-7），本实现用 rolling std，对 50 位精度基准误差为 0。项目实际使用的周期两者一致到 1e-10。 顺带清理 12 个文件中 16 处从未调用的 talib/technical 导入。 验证：9440 组随机对拨；真实 K 线端到端比对 add_indicators 全部 33 个 指标列，NaN 模式一致、MACD 柱符号 100% 相同；屏蔽两个包后 60 个模块 均可导入。新增 test_ta_compat.py 将输出逐 bar 钉在 TA-Lib 上，但该文件 在 TA-Lib 缺失时静默跳过，改动 ta.py 需在装有 TA-Lib 的环境复跑。 Co-authored-by: Cursor <cursoragent@cursor.com>
195 lines
7.6 KiB
Python
195 lines
7.6 KiB
Python
import warnings
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# 抑制 Docker 内 technical.util 的 fillna/ffill/bfill 的 pandas FutureWarning（pandas 2.x 弃用 object 静默 downcast）
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warnings.filterwarnings(
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"ignore",
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category=FutureWarning,
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message=".*Downcasting object dtype arrays on \\.fillna.*",
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)
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from datetime import timedelta
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from pandas import DataFrame
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from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX, Chan_MACD_STATE, Chan_PRICE_TREND, Chan_KLU_PATTERN
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from chanlun.core.ChanKLU import ChanKLU
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from chanlun.core.ChanKLC import ChanKLC
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from chanlun.core.ChanBI import ChanBI
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from chanlun.core.ChanSBI import ChanSBI
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from chanlun.core.ChanSEG import ChanSEG
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from chanlun.core.ChanZS import ChanZS
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from chanlun.core.ChanBSP import ChanBSP
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import pandas as pd
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from decimal import Decimal
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import numpy as np
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from chanlun.indicators.ChanMACD import ChanMACD
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from chanlun.pipeline.timeframe import TF_DF
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from chanlun.analysis.ChanZone import StructureZone, StructureZoneConfig, analyze_structure_zones
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class ChanLun():
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def __init__(self):
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self.time2m = 2
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self.time3m = 3
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self.time5m = 5
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self.time10m = 10
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self.time20m = 20
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self.time_m_intervals = [2, 3, 5, 10, 20]
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self.time_m_symbols = ['2m', '3m', '5m', '10m', '20m']
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self.time30m = 30
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self.time45m = 45
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self.time_m15_intervals = [30, 45]
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self.time_m15_symbols = ['30m', '45m']
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self.time2h = 2*60
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self.time4h = 4*60
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self.time6h = 6*60
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self.time8h = 8*60
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self.time12h = 12*60
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self.time16h = 16*60
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self.time_h_intervals = [2*60, 4*60, 6*60, 8*60, 12*60, 16*60]
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self.time_h_symbols = ['2h', '4h', '6h', '8h', '12h', '16h']
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self.time2d = 2*24*60
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self.time3d = 3*24*60
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self.time_d_intervals = [2*24*60, 3*24*60]
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self.time_d_symbols = ['2d', '3d']
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self.time1w = 7*24*60
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self.time2w = 14*24*60
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self.time_w_intervals = [14*24*60]
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self.time_w_symbols = ['2w']
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self.time2M = 2*30*24*60
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self.time3M = 3*30*24*60
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self.time6M = 6*30*24*60
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self.time1y = 12*30*24*60
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self.time_M_intervals = [2*30*24*60, 3*30*24*60, 6*30*24*60, 12*30*24*60]
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self.time_M_symbols = ['2M', '3M', '6M', '1y']
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self.time_symbols = ['1m', '2m', '3m', '5m', '10m', '15m', '20m', '30m', '45m','1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d']
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self.tf_df_dict = {}
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self.ema_symbols = ['5m', '15m', '30m', '45m', '1h', '2h', '4h', '8h', '12h', '1d', '2d', '3d']
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self.tf_df = TF_DF()
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def init_data(self, dataframe, intervals, timeframes):
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for index in range(0, len(intervals)):
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timeframe = timeframes[index]
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interval = intervals[index]
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self.tf_df_dict[timeframe] = TF_DF(dataframe, interval, timeframe)
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def init_dataframes(self, dataframe_m=None, dataframe_15m=None, dataframe_h=None, dataframe_d=None, dataframe_w=None, dataframe_M=None):
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self.tf_df_dict = {}
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if dataframe_m is not None:
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self.tf_df_dict['1m'] = TF_DF(dataframe_m, 1, '1m')
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self.init_data(dataframe_m, self.time_m_intervals, self.time_m_symbols)
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if dataframe_15m is not None:
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self.tf_df_dict['15m'] = TF_DF(dataframe_15m, 1, '15m')
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self.init_data(dataframe_15m, self.time_m15_intervals, self.time_m15_symbols)
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if dataframe_h is not None:
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self.tf_df_dict['1h'] = TF_DF(dataframe_h, 1, '1h')
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self.init_data(dataframe_h, self.time_h_intervals, self.time_h_symbols)
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if dataframe_d is not None:
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self.tf_df_dict['1d'] = TF_DF(dataframe_d, 1, '1d')
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self.init_data(dataframe_d, self.time_d_intervals, self.time_d_symbols)
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if dataframe_w is not None and False:
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self.tf_df_dict['1w'] = TF_DF(dataframe_w, 1, '1w')
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self.init_data(dataframe_w, self.time_w_intervals, self.time_w_symbols)
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if dataframe_M is not None and False:
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self.tf_df_dict['1M'] = TF_DF(dataframe_M, 1, '1M')
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self.init_data(dataframe_M, self.time_M_intervals, self.time_M_symbols)
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def get_ema52_dict(self):
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if len(self.tf_df_dict) > 0:
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return {key: self.tf_df_dict[key].get_ema52() for key in self.ema_symbols}
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return None
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def get_ema24_dict(self):
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if len(self.tf_df_dict) > 0:
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return {key: self.tf_df_dict[key].get_ema24() for key in self.ema_symbols}
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return None
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def get_current_klc_dict(self):
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if len(self.tf_df_dict) > 0:
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return {key: self.tf_df_dict[key].get_current_klc() for key in self.ema_symbols}
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return None
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def get_tf_df_by_timeframe(self, timeframe):
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if timeframe in self.tf_df_dict:
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return self.tf_df_dict[timeframe]
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return None
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def check_price_ema52(self, price):
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key_list = []
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if len(self.tf_df_dict) > 0:
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ema52_dict = self.get_ema52_dict()
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for key in self.ema_symbols:
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if ema52_dict[key] is not None:
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if abs(price - ema52_dict[key]) < 100:
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key_list.append(key)
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return key_list
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def get_ema_bsp(self, long_tf='1h', short_tf='15m'):
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if long_tf in self.tf_df_dict and short_tf in self.tf_df_dict:
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long_df = self.tf_df_dict[long_tf]
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short_df = self.tf_df_dict[short_tf]
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return long_df.get_ema_bsp(short_df)
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return None
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def get_bsp_state(self, dataframe):
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return self.tf_df.get_bsp_state(dataframe)
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def get_structure_zones(self, current_price=None, config=None):
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if config is None:
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config = StructureZoneConfig()
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return analyze_structure_zones(
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self.tf_df_dict,
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self.ema_symbols,
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current_price=current_price,
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config=config,
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)
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# TF_DF methods ------------------------------------------
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def get_ema_state(self, dataframe):
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return self.tf_df.get_ema_state(dataframe)
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def get_klu_state(self, dataframe):
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return self.tf_df.get_klu_state(dataframe)
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def check_fx(self, klc):
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return self.tf_df.check_fx(klc)
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def add_indicators1(self, df):
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return self.tf_df.add_indicators(df)
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def get_bi_list(self, dataframe):
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return self.tf_df.get_bi_list(dataframe)
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def get_kl_data(self, dataframe:DataFrame):
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return self.tf_df.cal_kl_data(dataframe)
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def cal_volume_ratio(self, dataframe, window=10):
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return self.tf_df.cal_volume_ratio(dataframe, window)
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def calculate_seg_zs(self, bi_list, seg_list):
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return self.get_seg_zs_list(bi_list, seg_list)
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def get_seg_list(self, bi_list):
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return self.tf_df.get_seg_list(bi_list)
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def cal_trend(self, klc_list):
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return self.tf_df.cal_trend(klc_list)
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def check_top_fx(self, last_bottom, klc):
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return self.tf_df.check_top_fx(last_bottom, klc)
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def check_bottom_fx(self, last_top, klc):
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return self.tf_df.check_bottom_fx(last_top, klc)
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def cal_bi_list(self, klc_list):
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return self.tf_df.cal_bi_list(klc_list)
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def find_first_bsp(self, bi_list, bi_zs_list):
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return self.tf_df.find_first_bsp(bi_list, bi_zs_list)
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def find_second_bsp(self, bi_list, first_bsp_list):
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return self.tf_df.find_second_bsp(bi_list, first_bsp_list)
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def find_all_bsp(self, bi_list, bi_zs_list):
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return self.tf_df.find_all_bsp(bi_list, bi_zs_list)
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def get_zs_list(self, bi_list, seg_list):
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return self.tf_df.get_zs_list(bi_list, seg_list)
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def cal_bi_zs(self, seg_list):
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return self.tf_df.cal_bi_zs(seg_list)
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def cal_bi_zs_list(self, bi_list):
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#return self.tf_df.cal_bi_zs(bi_list)
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return self.tf_df.cal_bi_zs_list(bi_list)
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def cal_bi_zs_list_pure(self, bi_list):
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return self.tf_df.cal_bi_zs_list_pure(bi_list)
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def init_stream(self, dataframe, interval=1, timeframe=None):
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self.tf_df.init_stream(dataframe, interval, timeframe)
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return self.tf_df
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def append_bar(self, row):
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return self.tf_df.append_bar(row)
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def replace_last_bar(self, row):
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return self.tf_df.replace_last_bar(row)
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def get_bi_zs_list(self, bi_list):
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return self.tf_df.get_bi_zs_list(bi_list)
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def get_decimal(self, value):
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return Decimal("{:.2f}".format(value))
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def get_klc_list(self, klu_list):
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return self.tf_df.get_klc_list(klu_list)
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def get_klu_list(self, dataframe):
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return self.tf_df.cal_klu_pattern(self.get_kl_data(dataframe)) |