# 通过以下方式使用期货版API
>>>from wolfquant.api.future import ApiStruct, MdApi, TraderApi

import pandas as pd
import numpy as np
import tushare as ts
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.svm import LinearSVC, SVC
from sklearn.metrics import confusion_matrix
from wolfquant.utils.factor_utils import Factor_pipeline
from wolfquant.factors import trade_factors as tf

# 获取数据
datasets = ts.get_k_data('000300', start='2005-01-01', index=True).set_index('date')
datasets.index = pd.to_datetime(datasets.index, format='%Y-%m-%d')

# 构建特征
datasets = Factor_pipeline(datasets).add(tf.SMA, 50)\
                                    .add(tf.EWMA, 200)\
                                    .add(tf.BBANDS, 50)\
                                    .add(tf.CCI, 20)\
                                    .add(tf.ForceIndex, 1)\
                                    .add(tf.EVM, 14)\
                                    .add(tf.ROC, 5)\
                                    .add(tf.LAGRETURN, 0)\
                                    .data.dropna()

# 构建标签
datasets['direction'] = np.sign(datasets['LAGRETURN_0']).shift(-1)
datasets = datasets.dropna()

# 构建训练集和测试集
X = datasets[datasets.columns[6:-2]]
y = datasets['direction']
start_test = '2012-01-01'
X_train = X.loc[:start_test]
X_test = X.loc[start_test:]
y_train = y.loc[:start_test]
y_test = y.loc[start_test:]

# 构建模型
print('Hit rates/Confusion Matrices:\n')
models = [('LR', LogisticRegression()),
          ('LDA', LinearDiscriminantAnalysis()),
          ('QDA', QuadraticDiscriminantAnalysis()),
          ('LSVC', LinearSVC()),
          ('RSVM', SVC()),
          ('RF', RandomForestClassifier(n_estimators=1000))]

# 遍历模型
for m in models:
    # 训练模型
    m[1].fit(X_train, y_train)
    # 预测模型
    pred = m[1].predict(X_test)
    # 输出hit-rate和交叉验证结果
    print("%s:\n%0.3f" % (m[0], m[1].score(X_test, y_test)))
    print("%s\n" % confusion_matrix(pred, y_test))

import datetime
import numpy as np

from wolfquant.backtest import Backtest
from wolfquant.data import HistoricCSVDataHandler
from wolfquant.event import SignalEvent, OrderEvent
from wolfquant.execution import SimulatedExecutionHandler
from wolfquant.portfolio import NaivePortfolio
from wolfquant.strategy import Strategy

# 创建策略
class BuyAndHoldStrategy(Strategy):
    """一直持有策略
    results:
    Total Return: -2.74%
    Sharpe Ratio: -0.05
    Max Drawdown: 25.00%
    Drawdown Duration: 584
    交易信号数: 1
    下单数: 1
    成交数: 1
    """
    def init(self):
        self.bought = dict([(symbol, False) for symbol in self.symbol_list])

    def handle_bar(self, bar_dict):
        for s in self.symbol_list:
            if self.bought[s] is False:
                self.order_shares(s, 10)
                self.bought[s] = True

# 运行策略
csv_dir = 'data/'
symbol_list = ['hs300']
initial_capital = 100000.0
start_date = datetime.datetime(2015, 4, 8, 0, 0, 0)
end_date = datetime.datetime(2017, 10, 27, 0, 0, 0)
heartbeat = 0.0
backtest = Backtest(csv_dir,
                    symbol_list,
                    initial_capital,
                    heartbeat,
                    start_date,
                    end_date,
                    HistoricCSVDataHandler,
                    SimulatedExecutionHandler,
                    NaivePortfolio,
                    MovingAverageCrossStrategy)
backtest.simulate_trading()