# 克隆自聚宽文章：https://www.joinquant.com/post/2709

# 标题：基于SVM的机器学习策略

# 作者：走得很慢的海龟

import math

import numpy as np

#from sklearn import preprocessing, cross_validation, svm

from sklearn import preprocessing, svm

import matplotlib.pyplot as plt

from matplotlib import style

import sklearn

import time

from datetime import datetime

import cPickle as pickle

def initialize(context):

    g.train=True

# year_date is for get_fundamentals

def train_data(year_date, index_date):

    Valuation=[]

    price=[]

    status=[]

    SZ1=get_index_stocks('399008.XSHE', date=index_date)

    SZ2=get_index_stocks('399012.XSHE', date=index_date)

    SH=get_index_stocks('399905.XSHE', date=index_date)

    tem_index=SZ1+SZ2+SH

    unix_30d=60*60*24*30

    unix_weekend=60*60*24*3

    q=query(

    income.code, income.pubDate, income.total_operating_revenue,

    income.total_operating_cost, income.administration_expense,

    income.operating_profit, income.non_operating_revenue,

    income.total_profit, income.net_profit, income.basic_eps,

    income.diluted_eps, income.total_composite_income

    ).filter(

    valuation.code.in_(tem_index))

    incm = get_fundamentals(q, statDate=year_date)

    date=incm['pubDate']

    index=incm['code']

    q=query(

    indicator

    ).filter(

    valuation.code.in_(index))

    indictor=get_fundamentals(q, statDate=year_date)

    del(indictor['code'], indictor['statDate'], indictor['pubDate'])

    for each in range(0,len(date)):

        q=query(

        valuation.pe_ratio, valuation.pb_ratio, valuation.circulating_market_cap

        ).filter(

        valuation.code==(index[each]))

        each_valuation=get_fundamentals(q, date=date[each])

        date_stamp = datetime.strptime(date[each], '%Y-%m-%d')

        unix=time.mktime(date_stamp.timetuple())

        unix_30_late=unix+unix_30d

        Valuation.append(each_valuation.iloc[0].tolist())

        p1=get_price(index[each], start_date=date[each],

        end_date=date[each], frequency='daily', fields='close')

        if not p1.empty:

            pass

        else:

            p1_weekend=datetime.fromtimestamp(unix-unix_weekend).strftime('%Y-%m-%d')

            p1=get_price(index[each], start_date=p1_weekend,

            end_date=p1_weekend, frequency='daily', fields='close')

        p1_30d=datetime.fromtimestamp(unix_30_late).strftime('%Y-%m-%d')

        p2=get_price(index[each], start_date=p1_30d,

        end_date=p1_30d, frequency='daily', fields='close')

        if not p2.empty:

            pass

        else:

            date_stamp2 = datetime.strptime(p1_30d, '%Y-%m-%d')

            unix2=time.mktime(date_stamp2.timetuple())

            unix2_weekend=unix2-unix_weekend

            p2_weekend=datetime.fromtimestamp(unix2_weekend).strftime('%Y-%m-%d')

            p2=get_price(index[each], start_date=p2_weekend,

            end_date=p2_weekend, frequency='daily', fields='close')

        dif =  p2.values / p1.values

        if dif > 1.1:

            s=1

        else:

            s=0

        status.append(s)

        price.append(p1.iloc[0].tolist())

    Valuation=pd.DataFrame(Valuation, columns=['pe','pb','cir_mkt_cap'])

    price=pd.DataFrame(price, columns=['price'])

    status=pd.DataFrame(status, columns=['status'])

    df=pd.concat([incm,Valuation,price,indictor,status], axis=1)

    del(df['pubDate'], df['statDate.1'], df['code'])

    #y=df['status'].values.tolist()

    #df=np.random.permutation(df)

    #del(df['status'], df['code'])

    #X=np.array(df.replace('NaN', 9999).values.tolist())

    #X=preprocessing.scale(X)

    return df

def fundamental(index):

    Valuation=[]

    price=[]

    status=[]

    q=query(

    income.total_operating_revenue,

    income.total_operating_cost, income.administration_expense,

    income.operating_profit, income.non_operating_revenue,

    income.total_profit, income.net_profit, income.basic_eps,

    income.diluted_eps, income.total_composite_income

    ).filter(

    valuation.code.in_(index))

    incm = get_fundamentals(q)

    q=query(

    valuation.pe_ratio, valuation.pb_ratio, valuation.circulating_market_cap

    ).filter(

    valuation.code.in_(index))

    Valuation=get_fundamentals(q)#.values.tolist()

    q=query(

    indicator

    ).filter(

    valuation.code.in_(index))

    indictor=get_fundamentals(q)#.values.tolist()

    index2=indictor['code']

    del(indictor['code'], indictor['statDate'], indictor['pubDate'], indictor['day'])

    for each in index2:

        p=attribute_history(each, 1, unit='1d', fields=['close'], skip_paused=True)

        price.append(p.iloc[0].tolist())

    price=pd.DataFrame(price, columns=['price'])

    df=pd.concat([incm,Valuation,price,indictor], axis=1)

    X=np.array(df.replace('NaN', 9999).values.tolist())

    X=preprocessing.scale(X)

    return X, index2

def handle_data(context, data):

    if g.train:

        index_date=str('2014-03-01')

        df1=train_data(str('2014q1'),index_date)

        df2=train_data(str('2014q2'),index_date)

        df3=train_data(str('2014q3'),index_date)

        df4=train_data(str('2014q4'),index_date)

        df=pd.concat([df1,df2,df3,df4], axis=0)

        df.iloc[np.random.permutation(len(df))]

        y=df['status'].values.tolist()

        del(df['status'])

        log.info("<===== shape of training dataset @ %s", str(df.shape))

        X=np.array(df.replace('NaN', 9999).values.tolist())

        X=preprocessing.scale(X)

        clf = svm.SVC(kernel=str("linear"), C=1.0)

        clf.fit(X, y)

        filename = "temp.pkl"

        pickle_file = open(filename, 'wb')

        pickle.dump(clf, pickle_file)

        pickle_file.close()

        g.train=False

    filename = "temp.pkl"

    pickle_file = open(filename, 'rb')

    clf = pickle.load(pickle_file)

    year=context.current_dt.year

    month=context.current_dt.month

    day=context.current_dt.day

    index_date=str(year)+'-'+str(month)+'-'+str(day)

    SZ1=get_index_stocks('399008.XSHE', date=index_date)

    SZ2=get_index_stocks('399012.XSHE', date=index_date)

    SH=get_index_stocks('399905.XSHE', date=index_date)

    index=SZ1+SZ2+SH

    X, index2=fundamental(index)

    for each in range(0, len(index2)):

        if clf.predict(X[each].reshape(1,X.shape[1]))[0] == 1 and index2[each] not in context.portfolio.positions.keys():

            log.info("===================Buying:", index2[each])

            order_target_value(index2[each], context.portfolio.cash/5)

        if clf.predict(X[each].reshape(1,X.shape[1]))[0] == 0 and index2[each] in context.portfolio.positions.keys():

            log.info("<<<<<<<<<<<<<<<<<<Holding:", context.portfolio.positions.keys())

            log.info("-------------------selling:", index2[each])

            order_target(index2[each], 0)

# 止损

    if context.portfolio.positions:

        for stock in context.portfolio.positions.keys():

            cur_price = data[stock].close

            position=context.portfolio.positions[stock]

            if cur_price > position.avg_cost * (1 + 0.5) or cur_price < position.avg_cost * (1 - 0.2):

                order_target(stock, 0)

                log.info("<<<<<<<<<<<", stock, "%s lose:", 1-cur_price/position.avg_cost)