import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler

security = '000001.XSHG'
prices = get_price(security, '2005-01-05', '2019-01-01', '1d', ['high', 'low', 'open'])
long = 18 #beta calculation period

def caculate_beta(long, prices):
    beta_list = [] #beta list
    R_list = [] #r squared list
    
    for i in range(long,len(prices)):
        X = prices.low[i-long:i].reshape(-1,1)
        y = prices.high[i-long:i].reshape(-1,1)
        reg = LinearRegression().fit(X,y)
        beta_list.append(reg.coef_[0][0])
        R_list.append(reg.score(X,y))
    
    R_list = np.asarray(R_list).reshape(-1,1)
    beta_list = np.asarray(beta_list).reshape(-1,1)
    #standardize beta
    scaler = StandardScaler()
    beta_std = scaler.fit_transform(beta_list)
    beta_rightdev = R_list*beta_list*beta_std
    
    return (beta_list,beta_std,beta_rightdev)
beta_list, beta_std, beta_rightdev = caculate_beta(long,prices)

sns.distplot(beta_std)
plt.show()

sns.distplot(beta_rightdev)
plt.show()

sns.distplot(beta_rightdev,hist_kws={'cumulative': True}, kde_kws={'cumulative': True})
plt.show()

hold_period = [10,30,60,90]

def calculate_return_df(price,hold_period):
    #price has to be a series
    origional_name = price.name
    #turn price series to df
    price = pd.DataFrame(price)
    for i in hold_period:
        return_arr = price[origional_name]/price[origional_name].shift(i)-1
        return_arr = return_arr.dropna().values
        for y in range(i):
            return_arr = np.append(return_arr,np.nan)
        price[str(y+1)+'_days_return'] = return_arr
    return price

return_df = calculate_return_df(prices.open,hold_period)    

return_df.head()

for _ in range(long):
    beta_list = np.append(np.nan,beta_list.reshape(1,-1)[0])

for _ in range(long):
    beta_std = np.append(np.nan,beta_std.reshape(1,-1)[0])

for _ in range(long):
    beta_rightdev = np.append(np.nan,beta_rightdev.reshape(1,-1)[0])

return_df['beta'] = beta_list
return_df['beta_standardized'] = beta_std
return_df['beta_rightdev'] = beta_rightdev

return_df.head()

return_df = return_df.dropna()

return_df.head()

y_list = ['10_days_return','30_days_return','60_days_return','90_days_return']
X_list = ['beta','beta_standardized','beta_rightdev']

fig , axes = plt.subplots(nrows=3,ncols=4,figsize=(30, 20))

plt.figure(20)
for x in range(1,len(X_list)+1):
    for y in range(1,len(y_list)+1):
        axes[x-1][y-1].scatter(return_df[X_list[x-1]],return_df[y_list[y-1]])
plt.show()

def plot_df(return_df,variable):
    analysis_df = return_df.loc[:,[variable,'10_days_return','30_days_return','60_days_return','90_days_return']]
    group_df = analysis_df.groupby(pd.cut(analysis_df[variable],20)).mean().drop([variable],axis=1)
    group_df.plot()
    plt.show()
plot_df(return_df,'beta')

plot_df(return_df,'beta_standardized')

plot_df(return_df,'beta_rightdev')

return_df.head()

df = return_df[return_df.beta_rightdev<-0.9].loc[:,y_list]>0
df.sum()/len(df)

hold_period = [0.7,1,1.5]

def bet_probability(hold_period, sign):
    if sign > 0:
        for i in hold_period:
            df = return_df[return_df.beta_rightdev>i].loc[:,y_list]>0
            print df.sum()/len(df)
            print ""
            print 'sample size is '+str(len(df))
            print ""
            print ">>>>>>>>>>>>>>>>>"
            
    else:
        for i in hold_period:
            df = return_df[return_df.beta_rightdev<i*-1].loc[:,y_list]>0
            print df.sum()/len(df)
            print ""
            print 'sample size is '+str(len(df))
            print ""
            print ">>>>>>>>>>>>>>>>>"

bet_probability(hold_period,1)

hold_period = [0.7,0.9,1.1]

bet_probability(hold_period,-1)