mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-09-20 09:31:12 +00:00
682 lines
19 KiB
Python
682 lines
19 KiB
Python
# QTPyLib: Quantitative Trading Python Library
|
|
# https://github.com/ranaroussi/qtpylib
|
|
#
|
|
# Copyright 2016-2018 Ran Aroussi
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
import warnings
|
|
from datetime import datetime, timedelta
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from pandas.core.base import PandasObject
|
|
|
|
|
|
# =============================================
|
|
warnings.simplefilter(action="ignore", category=RuntimeWarning)
|
|
|
|
# =============================================
|
|
|
|
|
|
def numpy_rolling_window(data, window):
|
|
shape = data.shape[:-1] + (data.shape[-1] - window + 1, window)
|
|
strides = data.strides + (data.strides[-1],)
|
|
return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
|
|
|
|
|
|
def numpy_rolling_series(func):
|
|
def func_wrapper(data, window, as_source=False):
|
|
series = data.values if isinstance(data, pd.Series) else data
|
|
|
|
new_series = np.empty(len(series)) * np.nan
|
|
calculated = func(series, window)
|
|
new_series[-len(calculated) :] = calculated
|
|
|
|
if as_source and isinstance(data, pd.Series):
|
|
return pd.Series(index=data.index, data=new_series)
|
|
|
|
return new_series
|
|
|
|
return func_wrapper
|
|
|
|
|
|
@numpy_rolling_series
|
|
def numpy_rolling_mean(data, window, as_source=False):
|
|
return np.mean(numpy_rolling_window(data, window), axis=-1)
|
|
|
|
|
|
@numpy_rolling_series
|
|
def numpy_rolling_std(data, window, as_source=False):
|
|
return np.std(numpy_rolling_window(data, window), axis=-1, ddof=1)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def session(df, start="17:00", end="16:00"):
|
|
"""remove previous globex day from df"""
|
|
if df.empty:
|
|
return df
|
|
|
|
# get start/end/now as decimals
|
|
int_start = list(map(int, start.split(":")))
|
|
int_start = (int_start[0] + int_start[1] - 1 / 100) - 0.0001
|
|
int_end = list(map(int, end.split(":")))
|
|
int_end = int_end[0] + int_end[1] / 100
|
|
int_now = df[-1:].index.hour[0] + (df[:1].index.minute[0]) / 100
|
|
|
|
# same-dat session?
|
|
is_same_day = int_end > int_start
|
|
|
|
# set pointers
|
|
curr = prev = df[-1:].index[0].strftime("%Y-%m-%d")
|
|
|
|
# globex/forex session
|
|
if not is_same_day:
|
|
prev = (datetime.strptime(curr, "%Y-%m-%d") - timedelta(1)).strftime("%Y-%m-%d")
|
|
|
|
# slice
|
|
if int_now >= int_start:
|
|
df = df[df.index >= curr + " " + start]
|
|
else:
|
|
df = df[df.index >= prev + " " + start]
|
|
|
|
return df.copy()
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def heikinashi(bars):
|
|
bars = bars.copy()
|
|
bars["ha_close"] = (bars["open"] + bars["high"] + bars["low"] + bars["close"]) / 4
|
|
|
|
# ha open
|
|
bars.at[0, "ha_open"] = (bars.at[0, "open"] + bars.at[0, "close"]) / 2
|
|
for i in range(1, len(bars)):
|
|
bars.at[i, "ha_open"] = (bars.at[i - 1, "ha_open"] + bars.at[i - 1, "ha_close"]) / 2
|
|
|
|
bars["ha_high"] = bars.loc[:, ["high", "ha_open", "ha_close"]].max(axis=1)
|
|
bars["ha_low"] = bars.loc[:, ["low", "ha_open", "ha_close"]].min(axis=1)
|
|
|
|
return pd.DataFrame(
|
|
index=bars.index,
|
|
data={
|
|
"open": bars["ha_open"],
|
|
"high": bars["ha_high"],
|
|
"low": bars["ha_low"],
|
|
"close": bars["ha_close"],
|
|
},
|
|
)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def tdi(series, rsi_lookback=13, rsi_smooth_len=2, rsi_signal_len=7, bb_lookback=34, bb_std=1.6185):
|
|
rsi_data = rsi(series, rsi_lookback)
|
|
rsi_smooth = sma(rsi_data, rsi_smooth_len)
|
|
rsi_signal = sma(rsi_data, rsi_signal_len)
|
|
|
|
bb_series = bollinger_bands(rsi_data, bb_lookback, bb_std)
|
|
|
|
return pd.DataFrame(
|
|
index=series.index,
|
|
data={
|
|
"rsi": rsi_data,
|
|
"rsi_signal": rsi_signal,
|
|
"rsi_smooth": rsi_smooth,
|
|
"rsi_bb_upper": bb_series["upper"],
|
|
"rsi_bb_lower": bb_series["lower"],
|
|
"rsi_bb_mid": bb_series["mid"],
|
|
},
|
|
)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def awesome_oscillator(df, weighted=False, fast=5, slow=34):
|
|
midprice = (df["high"] + df["low"]) / 2
|
|
|
|
if weighted:
|
|
ao = (midprice.ewm(fast).mean() - midprice.ewm(slow).mean()).values
|
|
else:
|
|
ao = numpy_rolling_mean(midprice, fast) - numpy_rolling_mean(midprice, slow)
|
|
|
|
return pd.Series(index=df.index, data=ao)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def nans(length=1):
|
|
mtx = np.empty(length)
|
|
mtx[:] = np.nan
|
|
return mtx
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def typical_price(bars):
|
|
res = (bars["high"] + bars["low"] + bars["close"]) / 3.0
|
|
return pd.Series(index=bars.index, data=res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def mid_price(bars):
|
|
res = (bars["high"] + bars["low"]) / 2.0
|
|
return pd.Series(index=bars.index, data=res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def ibs(bars):
|
|
"""Internal bar strength"""
|
|
res = np.round((bars["close"] - bars["low"]) / (bars["high"] - bars["low"]), 2)
|
|
return pd.Series(index=bars.index, data=res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def true_range(bars):
|
|
return pd.DataFrame(
|
|
{
|
|
"hl": bars["high"] - bars["low"],
|
|
"hc": abs(bars["high"] - bars["close"].shift(1)),
|
|
"lc": abs(bars["low"] - bars["close"].shift(1)),
|
|
}
|
|
).max(axis=1)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def atr(bars, window=14, exp=False):
|
|
tr = true_range(bars)
|
|
|
|
if exp:
|
|
res = rolling_weighted_mean(tr, window)
|
|
else:
|
|
res = rolling_mean(tr, window)
|
|
|
|
return pd.Series(res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def crossed(series1, series2, direction=None):
|
|
if isinstance(series1, np.ndarray):
|
|
series1 = pd.Series(series1)
|
|
|
|
if isinstance(series2, (float, int, np.ndarray, np.integer, np.floating)):
|
|
series2 = pd.Series(index=series1.index, data=series2)
|
|
|
|
if direction is None or direction == "above":
|
|
above = pd.Series((series1 > series2) & (series1.shift(1) <= series2.shift(1)))
|
|
|
|
if direction is None or direction == "below":
|
|
below = pd.Series((series1 < series2) & (series1.shift(1) >= series2.shift(1)))
|
|
|
|
if direction is None:
|
|
return above | below
|
|
|
|
return above if direction == "above" else below
|
|
|
|
|
|
def crossed_above(series1, series2):
|
|
return crossed(series1, series2, "above")
|
|
|
|
|
|
def crossed_below(series1, series2):
|
|
return crossed(series1, series2, "below")
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rolling_std(series, window=200, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
if min_periods == window and len(series) > window:
|
|
return numpy_rolling_std(series, window, True)
|
|
else:
|
|
try:
|
|
return series.rolling(window=window, min_periods=min_periods).std()
|
|
except Exception as e: # noqa: F841
|
|
return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rolling_mean(series, window=200, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
if min_periods == window and len(series) > window:
|
|
return numpy_rolling_mean(series, window, True)
|
|
else:
|
|
try:
|
|
return series.rolling(window=window, min_periods=min_periods).mean()
|
|
except Exception as e: # noqa: F841
|
|
return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rolling_min(series, window=14, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
try:
|
|
return series.rolling(window=window, min_periods=min_periods).min()
|
|
except Exception as e: # noqa: F841
|
|
return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rolling_max(series, window=14, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
try:
|
|
return series.rolling(window=window, min_periods=min_periods).max()
|
|
except Exception as e: # noqa: F841
|
|
return pd.Series(series).rolling(window=window, min_periods=min_periods).max()
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rolling_weighted_mean(series, window=200, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
try:
|
|
return series.ewm(span=window, min_periods=min_periods).mean()
|
|
except Exception as e: # noqa: F841
|
|
return pd.ewma(series, span=window, min_periods=min_periods)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def hull_moving_average(series, window=200, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
ma = (2 * rolling_weighted_mean(series, window / 2, min_periods)) - rolling_weighted_mean(
|
|
series, window, min_periods
|
|
)
|
|
return rolling_weighted_mean(ma, np.sqrt(window), min_periods)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def sma(series, window=200, min_periods=None):
|
|
return rolling_mean(series, window=window, min_periods=min_periods)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def wma(series, window=200, min_periods=None):
|
|
return rolling_weighted_mean(series, window=window, min_periods=min_periods)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def hma(series, window=200, min_periods=None):
|
|
return hull_moving_average(series, window=window, min_periods=min_periods)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def vwap(bars):
|
|
"""
|
|
calculate vwap of entire time series
|
|
(input can be pandas series or numpy array)
|
|
bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
|
|
"""
|
|
raise ValueError(
|
|
"using `qtpylib.vwap` facilitates lookahead bias. Please use "
|
|
"`qtpylib.rolling_vwap` instead, which calculates vwap in a rolling manner."
|
|
)
|
|
# typical = ((bars['high'] + bars['low'] + bars['close']) / 3).values
|
|
# volume = bars['volume'].values
|
|
|
|
# return pd.Series(index=bars.index,
|
|
# data=np.cumsum(volume * typical) / np.cumsum(volume))
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rolling_vwap(bars, window=200, min_periods=None):
|
|
"""
|
|
calculate vwap using moving window
|
|
(input can be pandas series or numpy array)
|
|
bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
|
|
"""
|
|
min_periods = window if min_periods is None else min_periods
|
|
|
|
typical = (bars["high"] + bars["low"] + bars["close"]) / 3
|
|
volume = bars["volume"]
|
|
|
|
left = (volume * typical).rolling(window=window, min_periods=min_periods).sum()
|
|
right = volume.rolling(window=window, min_periods=min_periods).sum()
|
|
|
|
return (
|
|
pd.Series(index=bars.index, data=(left / right))
|
|
.replace([np.inf, -np.inf], float("NaN"))
|
|
.ffill()
|
|
)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rsi(series, window=14):
|
|
"""
|
|
compute the n period relative strength indicator
|
|
"""
|
|
|
|
# 100-(100/relative_strength)
|
|
deltas = np.diff(series)
|
|
seed = deltas[: window + 1]
|
|
|
|
# default values
|
|
ups = seed[seed > 0].sum() / window
|
|
downs = -seed[seed < 0].sum() / window
|
|
rsival = np.zeros_like(series)
|
|
rsival[:window] = 100.0 - 100.0 / (1.0 + ups / downs)
|
|
|
|
# period values
|
|
for i in range(window, len(series)):
|
|
delta = deltas[i - 1]
|
|
if delta > 0:
|
|
upval = delta
|
|
downval = 0
|
|
else:
|
|
upval = 0
|
|
downval = -delta
|
|
|
|
ups = (ups * (window - 1) + upval) / window
|
|
downs = (downs * (window - 1.0) + downval) / window
|
|
rsival[i] = 100.0 - 100.0 / (1.0 + ups / downs)
|
|
|
|
# return rsival
|
|
return pd.Series(index=series.index, data=rsival)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def macd(series, fast=3, slow=10, smooth=16):
|
|
"""
|
|
compute the MACD (Moving Average Convergence/Divergence)
|
|
using a fast and slow exponential moving avg'
|
|
return value is emaslow, emafast, macd which are len(x) arrays
|
|
"""
|
|
macd_line = rolling_weighted_mean(series, window=fast) - rolling_weighted_mean(
|
|
series, window=slow
|
|
)
|
|
signal = rolling_weighted_mean(macd_line, window=smooth)
|
|
histogram = macd_line - signal
|
|
# return macd_line, signal, histogram
|
|
return pd.DataFrame(
|
|
index=series.index,
|
|
data={"macd": macd_line.values, "signal": signal.values, "histogram": histogram.values},
|
|
)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def bollinger_bands(series, window=20, stds=2):
|
|
ma = rolling_mean(series, window=window, min_periods=1)
|
|
std = rolling_std(series, window=window, min_periods=1)
|
|
upper = ma + std * stds
|
|
lower = ma - std * stds
|
|
|
|
return pd.DataFrame(index=series.index, data={"upper": upper, "mid": ma, "lower": lower})
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def weighted_bollinger_bands(series, window=20, stds=2):
|
|
ema = rolling_weighted_mean(series, window=window)
|
|
std = rolling_std(series, window=window)
|
|
upper = ema + std * stds
|
|
lower = ema - std * stds
|
|
|
|
return pd.DataFrame(
|
|
index=series.index, data={"upper": upper.values, "mid": ema.values, "lower": lower.values}
|
|
)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def returns(series):
|
|
try:
|
|
res = (series / series.shift(1) - 1).replace([np.inf, -np.inf], float("NaN"))
|
|
except Exception as e: # noqa: F841
|
|
res = nans(len(series))
|
|
|
|
return pd.Series(index=series.index, data=res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def log_returns(series):
|
|
try:
|
|
res = np.log(series / series.shift(1)).replace([np.inf, -np.inf], float("NaN"))
|
|
except Exception as e: # noqa: F841
|
|
res = nans(len(series))
|
|
|
|
return pd.Series(index=series.index, data=res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def implied_volatility(series, window=252):
|
|
try:
|
|
logret = np.log(series / series.shift(1)).replace([np.inf, -np.inf], float("NaN"))
|
|
res = numpy_rolling_std(logret, window) * np.sqrt(window)
|
|
except Exception as e: # noqa: F841
|
|
res = nans(len(series))
|
|
|
|
return pd.Series(index=series.index, data=res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def keltner_channel(bars, window=14, atrs=2):
|
|
typical_mean = rolling_mean(typical_price(bars), window)
|
|
atrval = atr(bars, window) * atrs
|
|
|
|
upper = typical_mean + atrval
|
|
lower = typical_mean - atrval
|
|
|
|
return pd.DataFrame(
|
|
index=bars.index,
|
|
data={"upper": upper.values, "mid": typical_mean.values, "lower": lower.values},
|
|
)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def roc(series, window=14):
|
|
"""
|
|
compute rate of change
|
|
"""
|
|
res = (series - series.shift(window)) / series.shift(window)
|
|
return pd.Series(index=series.index, data=res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def cci(series, window=14):
|
|
"""
|
|
compute commodity channel index
|
|
"""
|
|
price = typical_price(series)
|
|
typical_mean = rolling_mean(price, window)
|
|
res = (price - typical_mean) / (0.015 * np.std(typical_mean))
|
|
return pd.Series(index=series.index, data=res)
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
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
|