refactor: 以自实现指标替换 talib 与 technical 依赖
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>
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Ubuntu
@@ -14,12 +14,10 @@ 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 talib.abstract as ta
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import pandas as pd
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import matplotlib.pyplot as plt
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from matplotlib.dates import DateFormatter, date2num
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import matplotlib.patches as patches
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from technical.util import resample_to_interval
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from decimal import Decimal
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from chanlun.pipeline.orchestrator import ChanLun
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import xgboost as xgb
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@@ -2,7 +2,7 @@ import ccxt
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import pandas as pd
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import numpy as np
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import mplfinance as mpf
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from talib import MACD, SMA
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from chanlun.indicators import ta
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from datetime import datetime, timedelta
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import logging
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import datetime as dt
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@@ -249,8 +249,9 @@ def analyze_higher_timeframe(df_30m):
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# 8. Back-divergence detection (enhanced)
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def detect_back_divergence(df, strokes, higher_trend):
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try:
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macd, signal, hist = MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
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sma20 = SMA(df['Close'], timeperiod=20)
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macd_df = ta.MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
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macd, hist = macd_df['macd'], macd_df['macdhist']
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sma20 = ta.SMA(df['Close'], timeperiod=20)
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df['macd'] = macd
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df['hist'] = hist
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df['sma20'] = sma20
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@@ -0,0 +1,196 @@
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"""Drop-in replacement for the `talib.abstract` calls this project makes.
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Same call signatures, same column names, same NaN warm-up lengths, so call
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sites only change their import line.
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Only what the codebase actually uses is implemented: SMA, MA, EMA, RSI, ATR,
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MACD, BBANDS. Numerical agreement with TA-Lib is enforced by
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`chanlun/tests/test_ta_compat.py`, which skips when talib is absent.
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The warm-up conventions below are TA-Lib's, not the textbook ones, and they
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differ between functions — getting them wrong shifts every downstream Chan
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structure by a bar:
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SMA/BBANDS first value at index period-1
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EMA seeded with the SMA of the first `period` values, at index period-1
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RSI/ATR Wilder smoothing (alpha = 1/period), first value at index period
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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__all__ = ["SMA", "MA", "EMA", "RSI", "ATR", "MACD", "BBANDS"]
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def _series(data, price: str = "close") -> pd.Series:
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"""Accept the abstract-API shapes: DataFrame, Series, or ndarray."""
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if isinstance(data, pd.DataFrame):
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return data[price].astype(float)
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if isinstance(data, pd.Series):
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return data.astype(float)
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return pd.Series(np.asarray(data, dtype=float))
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def _recursive(values: np.ndarray, seed: float, start: int, alpha: float, n: int) -> np.ndarray:
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"""out[start] = seed; out[i] = alpha*values[i] + (1-alpha)*out[i-1].
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Delegates the recursion to pandas' C implementation rather than a Python
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loop — `research/` runs this over long histories.
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"""
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out = np.full(n, np.nan)
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if start >= n:
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return out
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tail = values[start:].astype(float).copy()
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tail[0] = seed
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out[start:] = pd.Series(tail).ewm(alpha=alpha, adjust=False).mean().to_numpy()
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return out
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def SMA(data, timeperiod: int = 30, price: str = "close") -> pd.Series:
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s = _series(data, price)
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return s.rolling(window=timeperiod, min_periods=timeperiod).mean()
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def MA(data, timeperiod: int = 30, matype: int = 0, price: str = "close") -> pd.Series:
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if matype != 0:
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raise NotImplementedError(f"MA matype={matype} is not used by this codebase")
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return SMA(data, timeperiod, price=price)
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def _ema(x: np.ndarray, period: int, start: int) -> np.ndarray:
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"""EMA whose first output lands on `start`, seeded by the SMA of the
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`period` values ending there.
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`start` is a parameter because MACD needs the fast EMA to begin later than
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it naturally would; see the note in MACD().
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"""
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n = x.size
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if n <= start or start < period - 1:
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return np.full(n, np.nan)
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seed = x[start - period + 1: start + 1].mean()
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return _recursive(x, seed, start, 2.0 / (period + 1.0), n)
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def EMA(data, timeperiod: int = 30, price: str = "close") -> pd.Series:
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s = _series(data, price)
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x = s.to_numpy(dtype=float)
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return pd.Series(_ema(x, timeperiod, timeperiod - 1), index=s.index)
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def RSI(data, timeperiod: int = 14, price: str = "close") -> pd.Series:
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s = _series(data, price)
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x = s.to_numpy(dtype=float)
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n = x.size
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out = np.full(n, np.nan)
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if n <= timeperiod:
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return pd.Series(out, index=s.index)
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delta = np.diff(x)
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gain = np.where(delta > 0.0, delta, 0.0)
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loss = np.where(delta < 0.0, -delta, 0.0)
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# delta[k] corresponds to bar k+1, so the first `timeperiod` deltas seed bar `timeperiod`.
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alpha = 1.0 / timeperiod
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avg_gain = _recursive(gain, gain[:timeperiod].mean(), timeperiod - 1, alpha, n - 1)
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avg_loss = _recursive(loss, loss[:timeperiod].mean(), timeperiod - 1, alpha, n - 1)
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ag = avg_gain[timeperiod - 1:]
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al = avg_loss[timeperiod - 1:]
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with np.errstate(divide="ignore", invalid="ignore"):
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rsi = np.where(al == 0.0, 100.0, 100.0 - 100.0 / (1.0 + ag / al))
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out[timeperiod:] = rsi
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return pd.Series(out, index=s.index)
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def ATR(data, timeperiod: int = 14) -> pd.Series:
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if not isinstance(data, pd.DataFrame):
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raise TypeError("ATR needs a DataFrame with high/low/close")
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high = data["high"].to_numpy(dtype=float)
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low = data["low"].to_numpy(dtype=float)
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close = data["close"].to_numpy(dtype=float)
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n = high.size
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out = np.full(n, np.nan)
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if n <= timeperiod:
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return pd.Series(out, index=data.index)
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prev_close = close[:-1]
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tr = np.maximum.reduce([
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high[1:] - low[1:],
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np.abs(high[1:] - prev_close),
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np.abs(low[1:] - prev_close),
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])
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# tr[k] is bar k+1; the first `timeperiod` true ranges seed bar `timeperiod`.
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smoothed = _recursive(tr, tr[:timeperiod].mean(), timeperiod - 1, 1.0 / timeperiod, n - 1)
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out[timeperiod:] = smoothed[timeperiod - 1:]
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return pd.Series(out, index=data.index)
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def MACD(
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data,
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fastperiod: int = 12,
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slowperiod: int = 26,
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signalperiod: int = 9,
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price: str = "close",
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) -> pd.DataFrame:
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if slowperiod < fastperiod:
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fastperiod, slowperiod = slowperiod, fastperiod
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s = _series(data, price)
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x = s.to_numpy(dtype=float)
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n = x.size
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macd = np.full(n, np.nan)
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signal = np.full(n, np.nan)
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hist = np.full(n, np.nan)
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empty = pd.DataFrame({"macd": macd, "macdsignal": signal, "macdhist": hist}, index=s.index)
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# Both EMAs emit their first value on the same bar. That makes the slow one
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# ordinary, but re-seeds the fast one from the SMA of the `fastperiod`
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# values ending there instead of carrying the recursion forward from bar
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# fastperiod-1 — the two disagree by ~0.2 on a 100-priced series.
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macd_start = slowperiod - 1
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if n <= macd_start:
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return empty
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line = _ema(x, fastperiod, macd_start) - _ema(x, slowperiod, macd_start)
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# The signal EMA runs over the MACD line, so everything shifts by another
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# signalperiod-1 bars, and TA-Lib trims the MACD line to match.
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valid = line[macd_start:]
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if valid.size < signalperiod:
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return empty
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sig = _recursive(
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valid, valid[:signalperiod].mean(), signalperiod - 1, 2.0 / (signalperiod + 1.0), valid.size
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)
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start = macd_start + signalperiod - 1
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macd[start:] = line[start:]
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signal[macd_start:] = sig
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hist = macd - signal
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return pd.DataFrame({"macd": macd, "macdsignal": signal, "macdhist": hist}, index=s.index)
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def BBANDS(
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data,
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timeperiod: int = 5,
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nbdevup: float = 2.0,
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nbdevdn: float = 2.0,
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matype: int = 0,
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price: str = "close",
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) -> pd.DataFrame:
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if matype != 0:
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raise NotImplementedError(f"BBANDS matype={matype} is not used by this codebase")
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s = _series(data, price)
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middle = s.rolling(window=timeperiod, min_periods=timeperiod).mean()
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# TA-Lib uses the population standard deviation.
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std = s.rolling(window=timeperiod, min_periods=timeperiod).std(ddof=0)
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return pd.DataFrame(
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{
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"upperband": middle + nbdevup * std,
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"middleband": middle,
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"lowerband": middle - nbdevdn * std,
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},
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index=s.index,
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)
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@@ -6,9 +6,7 @@ from decimal import Decimal
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from technical.util import resample_to_interval
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from chanlun.core.ChanBI import ChanBI
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from chanlun.core.ChanBIZS import ChanBIZS
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@@ -6,9 +6,7 @@ from decimal import Decimal
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from technical.util import resample_to_interval
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from chanlun.core.ChanBI import ChanBI
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from chanlun.core.ChanBIZS import ChanBIZS
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@@ -9,7 +9,7 @@ from datetime import datetime
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import pandas as pd
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from pandas import DataFrame
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from technical.util import resample_to_interval
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from chanlun.pipeline.resample import resample_to_interval
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from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_KLC_STATE
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from chanlun.core.ChanKLU import ChanKLU
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@@ -6,9 +6,8 @@ from decimal import Decimal
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from chanlun.indicators import ta
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from pandas import DataFrame
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from technical.util import resample_to_interval
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from chanlun.core.ChanBI import ChanBI
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from chanlun.core.ChanBIZS import ChanBIZS
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@@ -6,9 +6,7 @@ from decimal import Decimal
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from technical.util import resample_to_interval
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from chanlun.core.ChanBI import ChanBI
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from chanlun.core.ChanBIZS import ChanBIZS
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@@ -6,9 +6,7 @@ from decimal import Decimal
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from technical.util import resample_to_interval
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from chanlun.core.ChanBI import ChanBI
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from chanlun.core.ChanBIZS import ChanBIZS
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@@ -6,9 +6,7 @@ from decimal import Decimal
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from technical.util import resample_to_interval
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from chanlun.core.ChanBI import ChanBI
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from chanlun.core.ChanBIZS import ChanBIZS
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@@ -17,9 +17,7 @@ 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 talib.abstract as ta
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import pandas as pd
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from technical.util import resample_to_interval
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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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@@ -0,0 +1,52 @@
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"""OHLCV resampling — replaces `technical.util.resample_to_interval`.
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That was the only symbol this project imported from `technical`, which in turn
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pulled in the freqtrade dependency chain. Behaviour is preserved exactly,
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including the left-labelled bins (rows are candle *open* times) and the
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`dropna()` that drops empty intervals.
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"""
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from __future__ import annotations
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import pandas as pd
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__all__ = ["TICKER_INTERVAL_MINUTES", "resample_to_interval"]
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TICKER_INTERVAL_MINUTES: dict[str, int] = {
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"1m": 1,
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"5m": 5,
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"15m": 15,
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"30m": 30,
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"1h": 60,
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"60m": 60,
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"2h": 120,
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"4h": 240,
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"6h": 360,
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"12h": 720,
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"1d": 1440,
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"1w": 10080,
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}
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_OHLC_AGG = {
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"open": "first",
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"high": "max",
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"low": "min",
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"close": "last",
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"volume": "sum",
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}
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def resample_to_interval(dataframe: pd.DataFrame, interval: int | str) -> pd.DataFrame:
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"""Resample OHLCV rows to `interval` minutes (or a timeframe string).
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Merging the result back onto a finer frame requires care to avoid lookahead
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bias; this function only resamples.
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"""
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if isinstance(interval, str):
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interval = TICKER_INTERVAL_MINUTES[interval]
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df = dataframe.copy()
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df = df.set_index(pd.DatetimeIndex(df["date"]))
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df = df.resample(f"{interval}min", label="left").agg(_OHLC_AGG).dropna()
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df.reset_index(inplace=True)
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return df
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@@ -2,9 +2,8 @@ from datetime import timedelta
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from technical.util import resample_to_interval
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from chanlun.pipeline.resample import resample_to_interval
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from chanlun.core.ChanBI import ChanBI
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from chanlun.core.ChanBIZS import ChanBIZS
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@@ -0,0 +1,198 @@
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"""Pin chanlun.indicators.ta to TA-Lib's output, bar for bar.
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These indicators feed the Chan structure builders, so a one-bar shift in the
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warm-up or a different smoothing seed silently changes every downstream
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bi/seg/zs. Equality against the reference implementation is the only check
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that catches that.
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Skipped when talib is unavailable — which is the point of the replacement, so
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the suite still has to pass without it. Run in an environment that has talib
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whenever chanlun/indicators/ta.py changes.
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"""
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from __future__ import annotations
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import sys
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import unittest
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from pathlib import Path
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import numpy as np
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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from chanlun.indicators import ta # noqa: E402
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from chanlun.pipeline.resample import resample_to_interval # noqa: E402
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try:
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import talib.abstract as reference
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except ImportError: # pragma: no cover
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reference = None
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requires_talib = unittest.skipIf(reference is None, "talib not installed")
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def make_ohlcv(n: int = 900, seed: int = 7) -> pd.DataFrame:
|
||||
"""Random walk with enough range for BBANDS(365) and EMA(208) to warm up."""
|
||||
rng = np.random.default_rng(seed)
|
||||
close = 100.0 + np.cumsum(rng.normal(0.0, 1.0, n))
|
||||
spread = np.abs(rng.normal(0.0, 0.6, n)) + 0.05
|
||||
high = close + spread
|
||||
low = close - spread
|
||||
open_ = np.concatenate([[close[0]], close[:-1]])
|
||||
return pd.DataFrame(
|
||||
{
|
||||
"date": pd.date_range("2024-01-01", periods=n, freq="1min", tz="UTC"),
|
||||
"open": open_,
|
||||
"high": np.maximum.reduce([high, open_, close]),
|
||||
"low": np.minimum.reduce([low, open_, close]),
|
||||
"close": close,
|
||||
"volume": rng.uniform(1.0, 100.0, n),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
class TAEquivalence(unittest.TestCase):
|
||||
def setUp(self) -> None:
|
||||
self.df = make_ohlcv()
|
||||
|
||||
def assertSameSeries(self, got, expected, label: str) -> None:
|
||||
g = np.asarray(got, dtype=float)
|
||||
e = np.asarray(expected, dtype=float)
|
||||
self.assertEqual(g.shape, e.shape, f"{label}: shape")
|
||||
np.testing.assert_array_equal(
|
||||
np.isnan(g), np.isnan(e), err_msg=f"{label}: NaN warm-up differs"
|
||||
)
|
||||
mask = ~np.isnan(e)
|
||||
np.testing.assert_allclose(
|
||||
g[mask], e[mask], rtol=1e-9, atol=1e-8, err_msg=f"{label}: values differ"
|
||||
)
|
||||
|
||||
@requires_talib
|
||||
def test_sma(self) -> None:
|
||||
for period in (5, 20, 90, 250):
|
||||
self.assertSameSeries(
|
||||
ta.SMA(self.df, timeperiod=period),
|
||||
reference.SMA(self.df, timeperiod=period),
|
||||
f"SMA({period})",
|
||||
)
|
||||
|
||||
@requires_talib
|
||||
def test_ma(self) -> None:
|
||||
for period in (5, 10, 250):
|
||||
self.assertSameSeries(
|
||||
ta.MA(self.df, timeperiod=period),
|
||||
reference.MA(self.df, timeperiod=period),
|
||||
f"MA({period})",
|
||||
)
|
||||
|
||||
@requires_talib
|
||||
def test_ema(self) -> None:
|
||||
for period in (5, 7, 10, 13, 24, 26, 30, 52, 104, 156, 208):
|
||||
self.assertSameSeries(
|
||||
ta.EMA(self.df, timeperiod=period),
|
||||
reference.EMA(self.df, timeperiod=period),
|
||||
f"EMA({period})",
|
||||
)
|
||||
|
||||
@requires_talib
|
||||
def test_rsi(self) -> None:
|
||||
for period in (7, 14, 21):
|
||||
self.assertSameSeries(
|
||||
ta.RSI(self.df, timeperiod=period),
|
||||
reference.RSI(self.df, timeperiod=period),
|
||||
f"RSI({period})",
|
||||
)
|
||||
|
||||
@requires_talib
|
||||
def test_atr(self) -> None:
|
||||
for period in (7, 14, 30):
|
||||
self.assertSameSeries(
|
||||
ta.ATR(self.df, timeperiod=period),
|
||||
reference.ATR(self.df, timeperiod=period),
|
||||
f"ATR({period})",
|
||||
)
|
||||
|
||||
@requires_talib
|
||||
def test_macd(self) -> None:
|
||||
for fast, slow, signal in ((12, 26, 9), (26, 52, 9), (5, 35, 5)):
|
||||
got = ta.MACD(self.df, fastperiod=fast, slowperiod=slow, signalperiod=signal)
|
||||
exp = reference.MACD(self.df, fastperiod=fast, slowperiod=slow, signalperiod=signal)
|
||||
for col in ("macd", "macdsignal", "macdhist"):
|
||||
self.assertSameSeries(got[col], exp[col], f"MACD({fast},{slow},{signal}).{col}")
|
||||
|
||||
@requires_talib
|
||||
def test_bbands(self) -> None:
|
||||
cases = (
|
||||
(365, 3.0, 3.0),
|
||||
(120, 3.0, 3.0),
|
||||
(41, 2.3, 2.3),
|
||||
(41, 2.0, 2.0),
|
||||
(26, 3.0, 3.0),
|
||||
(20, 2.0, 2.0),
|
||||
(14, 2.0, 2.0),
|
||||
)
|
||||
for period, up, dn in cases:
|
||||
got = ta.BBANDS(self.df, timeperiod=period, nbdevup=up, nbdevdn=dn, matype=0)
|
||||
exp = reference.BBANDS(self.df, timeperiod=period, nbdevup=up, nbdevdn=dn, matype=0)
|
||||
for col in ("upperband", "middleband", "lowerband"):
|
||||
self.assertSameSeries(got[col], exp[col], f"BBANDS({period},{up},{dn}).{col}")
|
||||
|
||||
@requires_talib
|
||||
def test_bbands_is_more_accurate_than_talib_on_tiny_windows(self) -> None:
|
||||
"""A deliberate divergence, documented so nobody "fixes" it back.
|
||||
|
||||
TA-Lib derives the variance from sumsq/n - mean**2, which cancels
|
||||
catastrophically when the window is short and prices are far from zero;
|
||||
at timeperiod=2 it drifts ~1e-6. Rolling std is accurate there, so the
|
||||
two disagree. No timeperiod below 14 is used in this codebase, and the
|
||||
periods that are used agree to ~1e-10 (covered by test_bbands).
|
||||
"""
|
||||
got = ta.BBANDS(self.df, timeperiod=2, nbdevup=1.0, nbdevdn=1.0, matype=0)["upperband"]
|
||||
exp = reference.BBANDS(self.df, timeperiod=2, nbdevup=1.0, nbdevdn=1.0, matype=0)["upperband"]
|
||||
|
||||
window = self.df["close"].rolling(2)
|
||||
truth = window.mean() + window.std(ddof=0)
|
||||
ours = np.nanmax(np.abs((got - truth).to_numpy()))
|
||||
theirs = np.nanmax(np.abs((exp - truth).to_numpy()))
|
||||
self.assertLess(ours, 1e-9)
|
||||
self.assertLess(ours, theirs)
|
||||
|
||||
@requires_talib
|
||||
def test_matches_on_real_price_scale(self) -> None:
|
||||
"""Guard against tolerances that only hold near 100."""
|
||||
df = self.df.copy()
|
||||
for col in ("open", "high", "low", "close"):
|
||||
df[col] *= 900.0
|
||||
self.assertSameSeries(
|
||||
ta.ATR(df, timeperiod=14), reference.ATR(df, timeperiod=14), "ATR@scale"
|
||||
)
|
||||
self.assertSameSeries(
|
||||
ta.RSI(df, timeperiod=14), reference.RSI(df, timeperiod=14), "RSI@scale"
|
||||
)
|
||||
|
||||
|
||||
class ResampleEquivalence(unittest.TestCase):
|
||||
@unittest.skipIf(
|
||||
__import__("importlib").util.find_spec("technical") is None,
|
||||
"technical not installed",
|
||||
)
|
||||
def test_matches_technical(self) -> None:
|
||||
from technical.util import resample_to_interval as ref_resample
|
||||
|
||||
df = make_ohlcv(600)
|
||||
for interval in (5, 15, 60, "5m", "1h"):
|
||||
got = resample_to_interval(df, interval)
|
||||
exp = ref_resample(df, interval)
|
||||
pd.testing.assert_frame_equal(got, exp, check_exact=False, rtol=1e-12)
|
||||
|
||||
def test_shapes_without_reference(self) -> None:
|
||||
df = make_ohlcv(120)
|
||||
out = resample_to_interval(df, 5)
|
||||
self.assertEqual(list(out.columns), ["date", "open", "high", "low", "close", "volume"])
|
||||
self.assertLessEqual(len(out), 120 // 5 + 1)
|
||||
self.assertTrue((out["high"] >= out["low"]).all())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -574,7 +574,7 @@ class ChinaStockData:
|
||||
def add_indicators(self, df):
|
||||
"""添加技术指标"""
|
||||
try:
|
||||
import talib.abstract as ta
|
||||
from chanlun.indicators import ta
|
||||
import numpy as np
|
||||
|
||||
# MACD指标
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import talib.abstract as ta
|
||||
from chanlun.indicators import ta
|
||||
|
||||
from chanlun import TF_DF
|
||||
from chanlun.core.ChanEnum import Chan_KLC_FX, Chan_FX_TYPE
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import talib.abstract as ta
|
||||
from chanlun.indicators import ta
|
||||
from . import state
|
||||
|
||||
def add_indicators(df):
|
||||
|
||||
Reference in New Issue
Block a user