139 lines
4.8 KiB
Python
139 lines
4.8 KiB
Python
import numpy as np
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import math
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import akshare as ak
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import os
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from datetime import datetime, timedelta, date
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import pandas as pd
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import mplfinance as mpf
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def calc_sma_atr_pd(kdf,period):
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"""计算TR与ATR
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Args:
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kdf (_type_): 历史数据
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period (_type_): ATR周期
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Returns:
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_type_: 返回kdf，增加TR与ATR列
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"""
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kdf['HL'] = kdf['最高'] - kdf['最低']
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kdf['HC'] = np.abs(kdf['最高'] - kdf['收盘'].shift(1))
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kdf['LC'] = np.abs(kdf['最低'] - kdf['收盘'].shift(1))
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kdf['TR'] = np.round(kdf[['HL','HC','LC']].max(axis=1), 3)
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# ranges = pd.concat([high_low, high_close, low_close], axis=1)
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# true_range = np.max(ranges, axis=1)
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kdf['ATR'] = np.round(kdf['TR'].rolling(period).mean(), 3)
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return kdf.drop(['HL','HC','LC'], axis = 1)
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class TurtleTrading(object):
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"""对象范围较小，对某一个标的创建一个海龟，如513300，
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计算ATR、
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Position Size，
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买入、卖出、加仓等行为
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Args:
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object (_type_): _description_
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"""
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def __init__(self, TradeCode) -> None:
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self.TradeCode = TradeCode
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def GetRecentData(self):
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"""获取某个标的的最近数据，从两年前到今天, 计算后的数据保存在self.CurrentData
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Returns:
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_type_: _description_
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"""
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Today = datetime.today()
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# print(Today)
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formatted_date = Today.strftime("%Y%m%d")
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two_years_ago = date.today() - timedelta(days=365*2).strftime("%Y%m%d")
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# print(formatted_date)
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Code = f"{self.TradeCode}"
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self.CurrentData = ak.fund_etf_hist_em(symbol=Code, period="daily", start_date=two_years_ago, end_date=formatted_date, adjust="")
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# return CurrentData
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def CalATR(self, data, ATRday, SaveOrNot):
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"""计算某个标的的ATR，从上市日到今天, 计算后的数据保存在self.CurrentData
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Args:
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ATRday: 多少日ATR
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SaveOrNot (_type_): 是否保存.csv数据
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"""
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self.CurrentData = calc_sma_atr_pd(data, ATRday)
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self.N = self.CurrentData['ATR']
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if SaveOrNot:
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self.CurrentData.to_csv('513300data-N.csv', index=False)
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print("csv保存成功")
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return self.N
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def CalPositionSize(self, RiskCoef, Capital):
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"""计算PosizionSize 持有的单位，该单位某标的，1N波动对应RiskCoef * Capital资金
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Args:
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RiskCoef (_type_): 风险系数
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Capital (_type_): 资金
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"""
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N = self.CurrentData.iloc[-1]['ATR']
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# N = 0.473
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Price = self.CurrentData.iloc[-1]['收盘']
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# Price = 5.60
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self.PositionSize = RiskCoef * Capital /( N*100*Price) # 默认用股票形式了 100
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return self.PositionSize
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def ReadExistData(self, data):
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"""除了通过发请求获取数据，也可以读本地的数据库，赋值给self.CurrentData
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Args:
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data (_type_): 本地csv名称
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"""
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self.CurrentData = pd.read_csv(data)
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def DrawKLine(self, days):
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"""画出k线图看看,画出最近days天的K线图
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"""
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# 日期部分
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dates = pd.to_datetime(self.CurrentData['日期'][-days:])
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# Klinedf['Data'] = pd.to_datetime(self.CurrentData['日期'])
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Klinedf = pd.DataFrame()
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# Klinedf.set_index = Klinedf['Data']
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# 其他数据
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Klinedf['Open'] = self.CurrentData['开盘'][-days:]
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Klinedf['High'] = self.CurrentData['最高'][-days:]
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Klinedf['Low'] = self.CurrentData['最低'][-days:]
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Klinedf['Close'] = self.CurrentData['收盘'][-days:]
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Klinedf['Volume'] = self.CurrentData['成交量'][-days:]
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Klinedf.set_index(dates, inplace=True)
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# 画图
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mpf.plot(Klinedf, type='candle', style='yahoo', volume=False, mav=(5,), addplot=[mpf.make_addplot(self.Donchian[['Upper', 'Lower']])])
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def calculate_donchian_channel(self, days, n):
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"""
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计算唐奇安通道days一共多少日， n多少日唐奇安
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参数:
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self.CurrentData (DataFrame): 包含价格数据的Pandas DataFrame，至少包含"High"和"Low"列
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n (int): 时间周期
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返回:self.Donchian
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DataFrame: 唐奇安通道的DataFrame，包含"Upper", "Lower", 和 "Middle"列
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"""
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Donchian = pd.DataFrame()
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# 计算最高价和最低价的N日移动平均线
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Donchian['Upper'] = self.CurrentData['最高'][-days:].rolling(n).max()
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Donchian['Lower'] = self.CurrentData['最低'][-days:].rolling(n).min()
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# # 计算中间线
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# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2
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return Donchian |