358 lines
10 KiB
Python
358 lines
10 KiB
Python
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from datetime import datetime
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import warnings
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import numpy as np
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import pandas as pd
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from pandas import DataFrame
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import matplotlib.pyplot as plt
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from statsmodels.stats.diagnostic import acorr_ljungbox
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from statsmodels.tsa.stattools import adfuller as ADF
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from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
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import talib
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from vnpy.trader.constant import Exchange, Interval
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from vnpy.trader.database import database_manager
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warnings.filterwarnings("ignore")
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class DataAnalysis:
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def __init__(self):
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""""""
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self.symbol = ""
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self.exchange = None
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self.interval = None
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self.start = None
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self.end = None
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self.rate = 0.0
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self.window_volatility = 20
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self.window_index = 20
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self.orignal = pd.DataFrame()
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self.index_1to1 = []
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self.index_2to2 = []
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self.index_3to1 = []
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self.index_2to1 = []
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self.index_4to1 = []
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self.intervals = []
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self.results = {}
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def load_history(
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self,
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symbol: str,
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exchange: Exchange,
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interval: Interval,
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start: datetime,
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end: datetime,
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rate: float = 0.0,
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index_1to1: list = None,
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index_2to2: list = None,
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index_3to1: list = None,
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index_2to1: list = None,
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index_4to1: list = None,
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window_index: int = 20,
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window_volatility: int = 20,
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):
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""""""
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output("开始加载历史数据")
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self.window_volatility = window_volatility
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self.window_index = window_index
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self.rate = rate
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self.index_1to1 = index_1to1
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self.index_2to2 = index_2to2
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self.index_3to1 = index_3to1
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self.index_2to1 = index_2to1
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self.index_4to1 = index_4to1
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# Load history data from database
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bars = database_manager.load_bar_data(
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symbol=symbol,
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exchange=exchange,
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interval=interval,
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start=start,
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end=end,
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)
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output(f"历史数据加载完成，数据量：{len(bars)}")
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# Generate history data in DataFrame
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t = []
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o = []
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h = []
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l = []
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c = []
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v = []
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for bar in bars:
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time = bar.datetime
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open_price = bar.open_price
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high_price = bar.high_price
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low_price = bar.low_price
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close_price = bar.close_price
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volume = bar.volume
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t.append(time)
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o.append(open_price)
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h.append(high_price)
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l.append(low_price)
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c.append(close_price)
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v.append(volume)
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self.orignal["open"] = o
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self.orignal["high"] = h
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self.orignal["low"] = l
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self.orignal["close"] = c
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self.orignal["volume"] = v
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self.orignal.index = t
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def base_analysis(self, df: DataFrame = None):
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""""""
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if df is None:
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df = self.orignal
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if df is None:
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output("数据为空，请输入数据")
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close_price = df["close"]
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output("第一步:画出行情图，检查数据断点")
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close_price.plot(figsize=(20, 8), title="close_price")
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plt.show()
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random_test(close_price)
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stability_test(close_price)
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autocorrelation_test(close_price)
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self.relative_volatility_analysis(df)
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self.growth_analysis(df)
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self.calculate_index(df)
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return df
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def relative_volatility_analysis(self, df: DataFrame = None):
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"""
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相对波动率
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"""
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output("第五步：相对波动率分析")
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df["volatility"] = talib.ATR(
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np.array(df["high"]),
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np.array(df["low"]),
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np.array(df["close"]),
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self.window_volatility
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)
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df["fixed_cost"] = df["close"] * self.rate
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df["relative_vol"] = df["volatility"] - df["fixed_cost"]
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df["relative_vol"].plot(figsize=(20, 6), title="relative volatility")
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plt.show()
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df["relative_vol"].hist(bins=200, figsize=(20, 6), grid=False)
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plt.show()
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statitstic_info(df["relative_vol"])
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def growth_analysis(self, df: DataFrame = None):
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"""
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百分比K线变化率
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"""
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output("第六步：变化率分析")
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df["pre_close"] = df["close"].shift(1).fillna(0)
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df["g%"] = 100 * (df["close"] - df["pre_close"]) / df["close"]
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df["g%"].plot(figsize=(20, 6), title="growth", ylim=(-5, 5))
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plt.show()
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df["g%"].hist(bins=200, figsize=(20, 6), grid=False)
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plt.show()
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statitstic_info(df["g%"])
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def calculate_index(self, df: DataFrame = None):
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""""""
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output("第七步：计算相关技术指标，返回DataFrame\n")
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if self.index_1to1:
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for i in self.index_1to1:
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func = getattr(talib, i)
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df[i] = func(
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np.array(df["close"]),
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self.window_index
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)
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if self.index_3to1:
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for i in self.index_3to1:
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func = getattr(talib, i)
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df[i] = func(
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np.array(df["high"]),
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np.array(df["low"]),
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np.array(df["close"]),
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self.window_index
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)
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if self.index_2to2:
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for i in self.index_2to2:
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func = getattr(talib, i)
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result_down, result_up = func(
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np.array(df["high"]),
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np.array(df["low"]),
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self.window_index
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)
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up = i + "_UP"
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down = i + "_DOWN"
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df[up] = result_up
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df[down] = result_down
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if self.index_2to1:
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for i in self.index_2to1:
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func = getattr(talib, i)
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df[i] = func(
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np.array(df["high"]),
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np.array(df["low"]),
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self.window_index
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)
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if self.index_4to1:
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for i in self.index_4to1:
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func = getattr(talib, i)
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df[i] = func(
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np.array(df["open"]),
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np.array(df["high"]),
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np.array(df["low"]),
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np.array(df["close"]),
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)
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return df
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def multi_time_frame_analysis(self, intervals: list = None, df: DataFrame = None):
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""""""
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if not intervals:
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output("请输入K线合成周期")
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return
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if df is None:
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df = self.orignal
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if df is None:
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output("请先加载数据")
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return
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for interval in intervals:
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output("------------------------------------------------")
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output(f"合成{interval}周期K先并开始数据分析")
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data = pd.DataFrame()
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data["open"] = df["open"].resample(interval, how="first")
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data["high"] = df["high"].resample(interval, how="max")
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data["low"] = df["low"].resample(interval, how="min")
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data["close"] = df["close"].resample(interval, how="last")
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data["volume"] = df["volume"].resample(interval, how="sum")
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result = self.base_analysis(data)
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self.results[interval] = result
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def show_chart(self, data, boll_wide):
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""""""
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data["boll_up"] = data["SMA"] + data["STDDEV"] * boll_wide
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data["boll_down"] = data["SMA"] - data["STDDEV"] * boll_wide
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up_signal = []
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down_signal = []
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len_data = len(data["close"])
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for i in range(1, len_data):
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if data.iloc[i]["close"] > data.iloc[i]["boll_up"]and data.iloc[i-1]["close"] < data.iloc[i - 1]["boll_up"]:
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up_signal.append(i)
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elif data.iloc[i]["close"] < data.iloc[i]["boll_down"] and data.iloc[i-1]["close"] > data.iloc[i - 1]["boll_down"]:
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down_signal.append(i)
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fig = plt.figure(figsize=(20, 8))
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close = data["close"]
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plt.plot(close, lw=1)
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plt.plot(close, '^', markersize=5, color='r', label='UP signal', markevery=up_signal)
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plt.plot(close, 'v', markersize=5, color='g', label='DOWN signal', markevery=down_signal)
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plt.plot(data["boll_up"], lw=0.5, color="r")
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plt.plot(data["boll_down"], lw=0.5, color="g")
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plt.legend()
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plt.show()
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data["ATR"].plot(figsize=(20, 3), title="ATR")
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plt.show()
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def random_test(close_price):
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""""""
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acorr_result = acorr_ljungbox(close_price, lags=1)
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p_value = acorr_result[1]
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if p_value < 0.05:
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output("第二步：随机性检验：非纯随机性")
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else:
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output("第二步：随机性检验：纯随机性")
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output(f"白噪声检验结果:{acorr_result}\n")
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def stability_test(close_price):
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""""""
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statitstic = ADF(close_price)
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t_s = statitstic[1]
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t_c = statitstic[4]["10%"]
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if t_s > t_c:
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output("第三步：平稳性检验：存在单位根，时间序列不平稳")
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else:
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output("第三步：平稳性检验：不存在单位根，时间序列平稳")
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output(f"ADF检验结果：{statitstic}\n")
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def autocorrelation_test(close_price):
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""""""
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output("第四步：画出自相关性图，观察自相关特性")
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plot_acf(close_price, lags=60)
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plt.show()
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plot_pacf(close_price, lags=60).show()
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plt.show()
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def statitstic_info(df):
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""""""
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mean = round(df.mean(), 4)
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median = round(df.median(), 4)
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output(f"样本平均数：{mean}, 中位数: {median}")
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skew = round(df.skew(), 4)
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kurt = round(df.kurt(), 4)
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if skew == 0:
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skew_attribute = "对称分布"
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elif skew > 0:
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skew_attribute = "分布偏左"
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else:
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skew_attribute = "分布偏右"
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if kurt == 0:
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kurt_attribute = "正态分布"
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elif kurt > 0:
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kurt_attribute = "分布陡峭"
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else:
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kurt_attribute = "分布平缓"
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output(f"偏度为：{skew}，属于{skew_attribute}；峰度为：{kurt}，属于{kurt_attribute}\n")
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def output(msg):
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"""
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Output message of backtesting engine.
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"""
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print(f"{datetime.now()}\t{msg}")
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