通过神经网络进行交易

来源：https://uqer.io/community/share/55b8acbaf9f06c91fa18c5ce

start = '2014-01-01'                       # 回测起始时间
end = '2015-05-25'                         # 回测结束时间
benchmark = 'HS300'                        # 策略参考标准
universe = set_universe('HS300')           # 证券池，支持股票和基金
capital_base = 1000000                     # 起始资金
freq = 'd'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 1                           # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟

import pybrain as brain
from pybrain.tools.shortcuts import buildNetwork
from pybrain.tools.customxml import NetworkReader
HISTORY      = 10                             # 通过前十日数据预测
fnn = buildNetwork(HISTORY, 15, 7, 1)         # 初始化神经网络

def initialize(account):                      # 初始化虚拟账户状态
    fnn = NetworkReader.readFrom('net.csv')

def handle_data(account):                     # 每个交易日的买入卖出指令
    hist = account.get_attribute_history('closePrice', 10)
    bucket = []
    for s in account.universe:
        sample = hist[s]
        possibility = fnn.activate(sample)
        bucket.append((possibility, s))

        if possibility < 0 and s in account.valid_secpos:
            order_to(s, 0)

    bucket = sorted(bucket, key=lambda x: x[0], reverse=True)
    print bucket[0][0]

    if bucket[0][0] < 0:
        raise Exception('Network Error')

    for s in bucket[:10]:
        if s[0] > 0.5 and s[1] not in account.valid_secpos:
            order(s[1], 10000 * s[0] * 80000)

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