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682 lines
19 KiB
Python
682 lines
19 KiB
Python
# QTPyLib: Quantitative Trading Python Library
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# https://github.com/ranaroussi/qtpylib
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#
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# Copyright 2016-2018 Ran Aroussi
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import warnings
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from datetime import datetime, timedelta
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import numpy as np
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import pandas as pd
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from pandas.core.base import PandasObject
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# =============================================
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warnings.simplefilter(action="ignore", category=RuntimeWarning)
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# =============================================
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def numpy_rolling_window(data, window):
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shape = data.shape[:-1] + (data.shape[-1] - window + 1, window)
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strides = data.strides + (data.strides[-1],)
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return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
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def numpy_rolling_series(func):
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def func_wrapper(data, window, as_source=False):
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series = data.values if isinstance(data, pd.Series) else data
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new_series = np.empty(len(series)) * np.nan
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calculated = func(series, window)
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new_series[-len(calculated) :] = calculated
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if as_source and isinstance(data, pd.Series):
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return pd.Series(index=data.index, data=new_series)
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return new_series
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return func_wrapper
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@numpy_rolling_series
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def numpy_rolling_mean(data, window, as_source=False):
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return np.mean(numpy_rolling_window(data, window), axis=-1)
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@numpy_rolling_series
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def numpy_rolling_std(data, window, as_source=False):
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return np.std(numpy_rolling_window(data, window), axis=-1, ddof=1)
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# ---------------------------------------------
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def session(df, start="17:00", end="16:00"):
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"""remove previous globex day from df"""
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if df.empty:
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return df
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# get start/end/now as decimals
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int_start = list(map(int, start.split(":")))
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int_start = (int_start[0] + int_start[1] - 1 / 100) - 0.0001
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int_end = list(map(int, end.split(":")))
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int_end = int_end[0] + int_end[1] / 100
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int_now = df[-1:].index.hour[0] + (df[:1].index.minute[0]) / 100
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# same-dat session?
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is_same_day = int_end > int_start
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# set pointers
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curr = prev = df[-1:].index[0].strftime("%Y-%m-%d")
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# globex/forex session
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if not is_same_day:
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prev = (datetime.strptime(curr, "%Y-%m-%d") - timedelta(1)).strftime("%Y-%m-%d")
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# slice
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if int_now >= int_start:
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df = df[df.index >= curr + " " + start]
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else:
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df = df[df.index >= prev + " " + start]
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return df.copy()
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# ---------------------------------------------
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def heikinashi(bars):
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bars = bars.copy()
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bars["ha_close"] = (bars["open"] + bars["high"] + bars["low"] + bars["close"]) / 4
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# ha open
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bars.at[0, "ha_open"] = (bars.at[0, "open"] + bars.at[0, "close"]) / 2
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for i in range(1, len(bars)):
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bars.at[i, "ha_open"] = (bars.at[i - 1, "ha_open"] + bars.at[i - 1, "ha_close"]) / 2
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bars["ha_high"] = bars.loc[:, ["high", "ha_open", "ha_close"]].max(axis=1)
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bars["ha_low"] = bars.loc[:, ["low", "ha_open", "ha_close"]].min(axis=1)
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return pd.DataFrame(
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index=bars.index,
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data={
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"open": bars["ha_open"],
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"high": bars["ha_high"],
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"low": bars["ha_low"],
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"close": bars["ha_close"],
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},
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)
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# ---------------------------------------------
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def tdi(series, rsi_lookback=13, rsi_smooth_len=2, rsi_signal_len=7, bb_lookback=34, bb_std=1.6185):
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rsi_data = rsi(series, rsi_lookback)
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rsi_smooth = sma(rsi_data, rsi_smooth_len)
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rsi_signal = sma(rsi_data, rsi_signal_len)
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bb_series = bollinger_bands(rsi_data, bb_lookback, bb_std)
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return pd.DataFrame(
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index=series.index,
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data={
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"rsi": rsi_data,
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"rsi_signal": rsi_signal,
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"rsi_smooth": rsi_smooth,
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"rsi_bb_upper": bb_series["upper"],
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"rsi_bb_lower": bb_series["lower"],
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"rsi_bb_mid": bb_series["mid"],
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},
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)
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# ---------------------------------------------
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def awesome_oscillator(df, weighted=False, fast=5, slow=34):
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midprice = (df["high"] + df["low"]) / 2
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if weighted:
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ao = (midprice.ewm(fast).mean() - midprice.ewm(slow).mean()).values
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else:
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ao = numpy_rolling_mean(midprice, fast) - numpy_rolling_mean(midprice, slow)
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return pd.Series(index=df.index, data=ao)
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# ---------------------------------------------
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def nans(length=1):
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mtx = np.empty(length)
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mtx[:] = np.nan
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return mtx
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# ---------------------------------------------
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def typical_price(bars):
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res = (bars["high"] + bars["low"] + bars["close"]) / 3.0
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return pd.Series(index=bars.index, data=res)
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# ---------------------------------------------
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def mid_price(bars):
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res = (bars["high"] + bars["low"]) / 2.0
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return pd.Series(index=bars.index, data=res)
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# ---------------------------------------------
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def ibs(bars):
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"""Internal bar strength"""
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res = np.round((bars["close"] - bars["low"]) / (bars["high"] - bars["low"]), 2)
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return pd.Series(index=bars.index, data=res)
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# ---------------------------------------------
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def true_range(bars):
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return pd.DataFrame(
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{
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"hl": bars["high"] - bars["low"],
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"hc": abs(bars["high"] - bars["close"].shift(1)),
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"lc": abs(bars["low"] - bars["close"].shift(1)),
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}
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).max(axis=1)
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# ---------------------------------------------
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def atr(bars, window=14, exp=False):
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tr = true_range(bars)
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if exp:
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res = rolling_weighted_mean(tr, window)
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else:
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res = rolling_mean(tr, window)
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return pd.Series(res)
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# ---------------------------------------------
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def crossed(series1, series2, direction=None):
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if isinstance(series1, np.ndarray):
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series1 = pd.Series(series1)
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if isinstance(series2, (float, int, np.ndarray, np.integer, np.floating)):
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series2 = pd.Series(index=series1.index, data=series2)
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if direction is None or direction == "above":
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above = pd.Series((series1 > series2) & (series1.shift(1) <= series2.shift(1)))
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if direction is None or direction == "below":
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below = pd.Series((series1 < series2) & (series1.shift(1) >= series2.shift(1)))
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if direction is None:
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return above | below
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return above if direction == "above" else below
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def crossed_above(series1, series2):
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return crossed(series1, series2, "above")
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def crossed_below(series1, series2):
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return crossed(series1, series2, "below")
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# ---------------------------------------------
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def rolling_std(series, window=200, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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if min_periods == window and len(series) > window:
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return numpy_rolling_std(series, window, True)
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else:
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try:
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return series.rolling(window=window, min_periods=min_periods).std()
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except Exception as e: # noqa: F841
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return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
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# ---------------------------------------------
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def rolling_mean(series, window=200, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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if min_periods == window and len(series) > window:
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return numpy_rolling_mean(series, window, True)
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else:
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try:
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return series.rolling(window=window, min_periods=min_periods).mean()
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except Exception as e: # noqa: F841
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return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
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# ---------------------------------------------
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def rolling_min(series, window=14, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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try:
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return series.rolling(window=window, min_periods=min_periods).min()
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except Exception as e: # noqa: F841
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return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
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# ---------------------------------------------
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def rolling_max(series, window=14, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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try:
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return series.rolling(window=window, min_periods=min_periods).max()
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except Exception as e: # noqa: F841
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return pd.Series(series).rolling(window=window, min_periods=min_periods).max()
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# ---------------------------------------------
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def rolling_weighted_mean(series, window=200, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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try:
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return series.ewm(span=window, min_periods=min_periods).mean()
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except Exception as e: # noqa: F841
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return pd.ewma(series, span=window, min_periods=min_periods)
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# ---------------------------------------------
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def hull_moving_average(series, window=200, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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ma = (2 * rolling_weighted_mean(series, window / 2, min_periods)) - rolling_weighted_mean(
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series, window, min_periods
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)
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return rolling_weighted_mean(ma, np.sqrt(window), min_periods)
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# ---------------------------------------------
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def sma(series, window=200, min_periods=None):
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return rolling_mean(series, window=window, min_periods=min_periods)
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# ---------------------------------------------
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def wma(series, window=200, min_periods=None):
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return rolling_weighted_mean(series, window=window, min_periods=min_periods)
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# ---------------------------------------------
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def hma(series, window=200, min_periods=None):
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return hull_moving_average(series, window=window, min_periods=min_periods)
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# ---------------------------------------------
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def vwap(bars):
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"""
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calculate vwap of entire time series
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(input can be pandas series or numpy array)
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bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
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"""
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raise ValueError(
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"using `qtpylib.vwap` facilitates lookahead bias. Please use "
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"`qtpylib.rolling_vwap` instead, which calculates vwap in a rolling manner."
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)
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# typical = ((bars['high'] + bars['low'] + bars['close']) / 3).values
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# volume = bars['volume'].values
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# return pd.Series(index=bars.index,
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# data=np.cumsum(volume * typical) / np.cumsum(volume))
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# ---------------------------------------------
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def rolling_vwap(bars, window=200, min_periods=None):
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"""
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calculate vwap using moving window
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(input can be pandas series or numpy array)
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bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
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"""
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min_periods = window if min_periods is None else min_periods
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typical = (bars["high"] + bars["low"] + bars["close"]) / 3
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volume = bars["volume"]
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left = (volume * typical).rolling(window=window, min_periods=min_periods).sum()
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right = volume.rolling(window=window, min_periods=min_periods).sum()
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return (
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pd.Series(index=bars.index, data=(left / right))
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.replace([np.inf, -np.inf], float("NaN"))
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.ffill()
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)
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# ---------------------------------------------
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def rsi(series, window=14):
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"""
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compute the n period relative strength indicator
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"""
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# 100-(100/relative_strength)
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deltas = np.diff(series)
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seed = deltas[: window + 1]
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# default values
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ups = seed[seed > 0].sum() / window
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downs = -seed[seed < 0].sum() / window
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rsival = np.zeros_like(series)
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rsival[:window] = 100.0 - 100.0 / (1.0 + ups / downs)
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# period values
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for i in range(window, len(series)):
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delta = deltas[i - 1]
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if delta > 0:
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upval = delta
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downval = 0
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else:
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upval = 0
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downval = -delta
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ups = (ups * (window - 1) + upval) / window
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downs = (downs * (window - 1.0) + downval) / window
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rsival[i] = 100.0 - 100.0 / (1.0 + ups / downs)
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# return rsival
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return pd.Series(index=series.index, data=rsival)
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# ---------------------------------------------
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def macd(series, fast=3, slow=10, smooth=16):
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"""
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compute the MACD (Moving Average Convergence/Divergence)
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using a fast and slow exponential moving avg'
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return value is emaslow, emafast, macd which are len(x) arrays
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"""
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macd_line = rolling_weighted_mean(series, window=fast) - rolling_weighted_mean(
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series, window=slow
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)
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signal = rolling_weighted_mean(macd_line, window=smooth)
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histogram = macd_line - signal
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# return macd_line, signal, histogram
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return pd.DataFrame(
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index=series.index,
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data={"macd": macd_line.values, "signal": signal.values, "histogram": histogram.values},
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)
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# ---------------------------------------------
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def bollinger_bands(series, window=20, stds=2):
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ma = rolling_mean(series, window=window, min_periods=1)
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std = rolling_std(series, window=window, min_periods=1)
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upper = ma + std * stds
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lower = ma - std * stds
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return pd.DataFrame(index=series.index, data={"upper": upper, "mid": ma, "lower": lower})
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# ---------------------------------------------
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def weighted_bollinger_bands(series, window=20, stds=2):
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ema = rolling_weighted_mean(series, window=window)
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std = rolling_std(series, window=window)
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upper = ema + std * stds
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lower = ema - std * stds
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return pd.DataFrame(
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index=series.index, data={"upper": upper.values, "mid": ema.values, "lower": lower.values}
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)
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# ---------------------------------------------
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def returns(series):
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try:
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res = (series / series.shift(1) - 1).replace([np.inf, -np.inf], float("NaN"))
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except Exception as e: # noqa: F841
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res = nans(len(series))
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return pd.Series(index=series.index, data=res)
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# ---------------------------------------------
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def log_returns(series):
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try:
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res = np.log(series / series.shift(1)).replace([np.inf, -np.inf], float("NaN"))
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except Exception as e: # noqa: F841
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res = nans(len(series))
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return pd.Series(index=series.index, data=res)
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# ---------------------------------------------
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def implied_volatility(series, window=252):
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try:
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logret = np.log(series / series.shift(1)).replace([np.inf, -np.inf], float("NaN"))
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res = numpy_rolling_std(logret, window) * np.sqrt(window)
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except Exception as e: # noqa: F841
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res = nans(len(series))
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return pd.Series(index=series.index, data=res)
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# ---------------------------------------------
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def keltner_channel(bars, window=14, atrs=2):
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typical_mean = rolling_mean(typical_price(bars), window)
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atrval = atr(bars, window) * atrs
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upper = typical_mean + atrval
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lower = typical_mean - atrval
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return pd.DataFrame(
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index=bars.index,
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data={"upper": upper.values, "mid": typical_mean.values, "lower": lower.values},
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)
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# ---------------------------------------------
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def roc(series, window=14):
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"""
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compute rate of change
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"""
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res = (series - series.shift(window)) / series.shift(window)
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return pd.Series(index=series.index, data=res)
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# ---------------------------------------------
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def cci(series, window=14):
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"""
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compute commodity channel index
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"""
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price = typical_price(series)
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typical_mean = rolling_mean(price, window)
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res = (price - typical_mean) / (0.015 * np.std(typical_mean))
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return pd.Series(index=series.index, data=res)
|
|
|
|
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|
# ---------------------------------------------
|
|
|
|
|
|
def stoch(df, window=14, d=3, k=3, fast=False):
|
|
"""
|
|
compute the n period relative strength indicator
|
|
http://excelta.blogspot.co.il/2013/09/stochastic-oscillator-technical.html
|
|
"""
|
|
|
|
my_df = pd.DataFrame(index=df.index)
|
|
|
|
my_df["rolling_max"] = df["high"].rolling(window).max()
|
|
my_df["rolling_min"] = df["low"].rolling(window).min()
|
|
|
|
my_df["fast_k"] = (
|
|
100 * (df["close"] - my_df["rolling_min"]) / (my_df["rolling_max"] - my_df["rolling_min"])
|
|
)
|
|
my_df["fast_d"] = my_df["fast_k"].rolling(d).mean()
|
|
|
|
if fast:
|
|
return my_df.loc[:, ["fast_k", "fast_d"]]
|
|
|
|
my_df["slow_k"] = my_df["fast_k"].rolling(k).mean()
|
|
my_df["slow_d"] = my_df["slow_k"].rolling(d).mean()
|
|
|
|
return my_df.loc[:, ["slow_k", "slow_d"]]
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def zlma(series, window=20, min_periods=None, kind="ema"):
|
|
"""
|
|
John Ehlers' Zero lag (exponential) moving average
|
|
https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average
|
|
"""
|
|
min_periods = window if min_periods is None else min_periods
|
|
|
|
lag = (window - 1) // 2
|
|
series = 2 * series - series.shift(lag)
|
|
if kind in ["ewm", "ema"]:
|
|
return wma(series, lag, min_periods)
|
|
elif kind == "hma":
|
|
return hma(series, lag, min_periods)
|
|
return sma(series, lag, min_periods)
|
|
|
|
|
|
def zlema(series, window, min_periods=None):
|
|
return zlma(series, window, min_periods, kind="ema")
|
|
|
|
|
|
def zlsma(series, window, min_periods=None):
|
|
return zlma(series, window, min_periods, kind="sma")
|
|
|
|
|
|
def zlhma(series, window, min_periods=None):
|
|
return zlma(series, window, min_periods, kind="hma")
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def zscore(bars, window=20, stds=1, col="close"):
|
|
"""get zscore of price"""
|
|
std = numpy_rolling_std(bars[col], window)
|
|
mean = numpy_rolling_mean(bars[col], window)
|
|
return (bars[col] - mean) / (std * stds)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def pvt(bars):
|
|
"""Price Volume Trend"""
|
|
trend = ((bars["close"] - bars["close"].shift(1)) / bars["close"].shift(1)) * bars["volume"]
|
|
return trend.cumsum()
|
|
|
|
|
|
def chopiness(bars, window=14):
|
|
atrsum = true_range(bars).rolling(window).sum()
|
|
highs = bars["high"].rolling(window).max()
|
|
lows = bars["low"].rolling(window).min()
|
|
return 100 * np.log10(atrsum / (highs - lows)) / np.log10(window)
|
|
|
|
|
|
# =============================================
|
|
|
|
|
|
PandasObject.session = session
|
|
PandasObject.atr = atr
|
|
PandasObject.bollinger_bands = bollinger_bands
|
|
PandasObject.cci = cci
|
|
PandasObject.crossed = crossed
|
|
PandasObject.crossed_above = crossed_above
|
|
PandasObject.crossed_below = crossed_below
|
|
PandasObject.heikinashi = heikinashi
|
|
PandasObject.hull_moving_average = hull_moving_average
|
|
PandasObject.ibs = ibs
|
|
PandasObject.implied_volatility = implied_volatility
|
|
PandasObject.keltner_channel = keltner_channel
|
|
PandasObject.log_returns = log_returns
|
|
PandasObject.macd = macd
|
|
PandasObject.returns = returns
|
|
PandasObject.roc = roc
|
|
PandasObject.rolling_max = rolling_max
|
|
PandasObject.rolling_min = rolling_min
|
|
PandasObject.rolling_mean = rolling_mean
|
|
PandasObject.rolling_std = rolling_std
|
|
PandasObject.rsi = rsi
|
|
PandasObject.stoch = stoch
|
|
PandasObject.zscore = zscore
|
|
PandasObject.pvt = pvt
|
|
PandasObject.chopiness = chopiness
|
|
PandasObject.tdi = tdi
|
|
PandasObject.true_range = true_range
|
|
PandasObject.mid_price = mid_price
|
|
PandasObject.typical_price = typical_price
|
|
PandasObject.vwap = vwap
|
|
PandasObject.rolling_vwap = rolling_vwap
|
|
PandasObject.weighted_bollinger_bands = weighted_bollinger_bands
|
|
PandasObject.rolling_weighted_mean = rolling_weighted_mean
|
|
|
|
PandasObject.sma = sma
|
|
PandasObject.wma = wma
|
|
PandasObject.ema = wma
|
|
PandasObject.hma = hma
|
|
|
|
PandasObject.zlsma = zlsma
|
|
PandasObject.zlwma = zlema
|
|
PandasObject.zlema = zlema
|
|
PandasObject.zlhma = zlhma
|
|
PandasObject.zlma = zlma
|