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cacu.py
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# -*- coding: utf-8 -*-
"""
Created on Fri May 11 15:13:02 2018
@author: Wentao
"""
import pandas as pd
import numpy as np
import statsmodels.api as sm #统计运算
import scipy.stats as scs #科学计算
import matplotlib.pyplot as plt #绘图
import bt
class SelectWhere(bt.Algo):
def __init__(self, signal):
self.signal = signal
def __call__(self, target):
# get signal on target.now
if target.now in self.signal.index:
sig = self.signal.ix[target.now]
# get indices where true as list
selected = list(sig.index[sig])
# save in temp - this will be used by the weighing algo
target.temp['selected'] = selected
return True
class Portfolio(object):
def __init__(self,startdate,enddate,coins,aid = 'market_cap',ra = 5):
self.startdate =startdate
self.enddate = enddate
self.coins = coins
self.aid=aid
self.ra = ra
def combine_data(self):
coin_price = {}
temp= {}
total =pd.DataFrame()
total_aid = pd.DataFrame()
startdate =self.startdate
enddate=self.enddate
for coin in self.coins:
temp1 = pd.read_csv(coin+'.csv')
coin_price[coin] = temp1
temp2 = temp1['Close**']
temp2.name = coin
temp2.index = temp1['Date'].apply(pd.Timestamp)
temp[coin] = temp2
total = pd.concat([total,temp2],axis=1,join='outer')
temp_aid = temp1[self.aid]
temp_aid.name = coin
temp_aid.index = temp1['Date'].apply(pd.Timestamp)
total_aid = pd.concat([total_aid,temp_aid],axis=1,join='outer')
print('combine '+coin+' over')
total=total.fillna(0)
total_aid =total_aid.fillna('0')
total = total[str(startdate):str(enddate)]
total_aid = total_aid[str(startdate):str(enddate)]
# total = total.apply(lambda x:x.apply(lambda x:x.replace(',','')))
total_aid = total_aid.apply(lambda x:x.apply(lambda x:x.replace(',','')))
self.data = total
self.data_aid = total_aid
# for coin in coin_names:
# temp1 = df.loc[df['cmc_symbol']==coin,:]
# coin_price[coin] = temp1
# temp2 = temp1['close']
# temp2.name = coin
# temp2.index = temp1['date'].apply(pd.Timestamp)
# total = pd.concat([total,temp2],axis=1,join='outer')
# temp_aid = temp1[self.aid]
# temp_aid.name = coin
# temp_aid.index = temp1['date'].apply(pd.Timestamp)
# total_aid = pd.concat([total_aid,temp_aid],axis=1,join='outer')
# print('combine '+coin+' over')
# total=total.fillna(0)
# total_aid =total_aid.fillna(0)
# self.data = total
# self.data_aid = total_aid
def caculate(self):
startdate = self.startdate
enddate = self.enddate
data = self.data
returns = (data-data.shift(1)) / data.shift(1)
n=365
##年化收益率
returns.mean()*n
##计算协方差矩阵
returns.cov()*n
##计算股票个数
noa=len(data.T)
##随机生成初始化权重
weights = np.random.random(noa)
##计算百分比
weights /= np.sum(weights)
weights
##下面通过一次蒙特卡洛模拟，产生大量随机的权重向量，并记录随机组合的预期收益和方差。
port_returns = []
port_variance = []
for p in range(20000):
print(str(p)+'-th begins.')
weights = np.random.random(noa)
weights /=np.sum(weights)
port_returns.append(np.sum(returns.mean()*n*weights))
port_variance.append(np.sqrt(np.dot(weights.T, np.dot(returns.cov()*n, weights))))
##因为要开更号，所以乘两次weight
##dot就是点乘
port_returns = np.array(port_returns)
port_variance = np.array(port_variance)
x_returns=[]
x_variance=[]
x_names=[]
for x in range(len(returns.columns)):
x_names.append(returns.columns[x])
x_returns.append(returns[returns.columns[x]].mean()*n)
x_variance.append(np.sqrt(returns[returns.columns[x]].var()*n))
x_returns=np.array(x_returns)
x_variance=np.array(x_variance)
#无风险利率设定为4%
risk_free = 0.04
plt.figure(figsize = (8,4))
plt.scatter(port_variance, port_returns, c=(port_returns-risk_free)/port_variance, marker = '.')
plt.grid(True)
plt.xlabel('excepted volatility')
plt.ylabel('expected return')
plt.colorbar(label = 'Sharpe ratio')
plt.scatter(x_variance,x_returns,c='r')
for i,txt in enumerate(x_names):
plt.annotate(txt,(x_variance[i],x_returns[i]))
plt.title(str(startdate)+' to '+str(enddate))
def benchmark_set(self,benchmark):
self.benchmark=benchmark
def backtest(self):
def benchmark_cacu(benchmark,name='bench_bitcoin'):
s = bt.Strategy(name, [bt.algos.RunOnce(),
bt.algos.SelectAll(),
bt.algos.WeighEqually(),
bt.algos.Rebalance()])
data = pd.DataFrame(self.data[benchmark])
print(data.head())
self.benchmark=benchmark
return bt.Backtest(s, data)
def above_sma(sma_per=50,name = 'above_sma'):
"""
Long securities that are above their n period
Simple Moving Averages with equal weights.
"""
# calc sma
data = self.data
sma = data.rolling(sma_per).mean()
# create strategy
s = bt.Strategy(name, [SelectWhere(data > sma),
bt.algos.WeighEqually(),
bt.algos.Rebalance()])
# now we create the backtest
return bt.Backtest(s, data)
def rank(ra,name = self.aid+str(self.ra)):
# calc rank
data = self.data
data_aid = self.data_aid
temp = data_aid.T
temp = temp.apply(lambda x:x.sort_values(ascending=False)[ra])
refer = pd.DataFrame([temp]*len(data_aid.columns)).T
refer.columns = data_aid.columns
# cacl signal
signal = data_aid > refer
# create strategy
s = bt.Strategy(name, [SelectWhere(signal),
bt.algos.WeighEqually(),
bt.algos.Rebalance()])
# now we create the backtest
return bt.Backtest(s, data)
rank = rank(self.ra, name = self.aid+str(self.ra))
# sma10 = above_sma(sma_per=10, name='sma10')
# sma20 = above_sma(sma_per=20, name='sma20')
sma40 = above_sma(sma_per=40, name='sma40')
benchmark = benchmark_cacu(self.benchmark, name='bench_bitcoin')
res = bt.run(sma40,benchmark,rank)
self.res = res