系统源代码发布v2.4供下载，带年化32.1%策略，简化GUI逻辑

底层替换了pybroker，其实我们仅依赖pandas(numpy)，大家读起代码更容易了。

现在我们要把传统一些经典的规则策略和系统实现一下。

有同学问，为何为直接把gplearn整合进来，deepalphagen端对端因子挖掘整合进来。

这是一个递进的过程。

机器学习时代，不意味着传统规则量化就完全失效了。

咱们的框架这么写指标，我们支持大家引入ta-lib或者tulipy（这个可以直接使用pip安装）

def SMA(arr: pd.Series, n: int) -> pd.Series:
    """
    Returns `n`-period simple moving average of array `arr`.
    """
    return pd.Series(arr).rolling(n).mean()


def HIGH(arr: pd.Series, n: int) -> pd.Series:
    return pd.Series(arr).shift(1).rolling(n).max()


def LOW(arr: pd.Series, n: int) -> pd.Series:
    return pd.Series(arr).shift(1).rolling(n).min()


import tulipy as ti


def ATR(high: pd.Series, low: pd.Series, close: pd.Series, n: int) -> pd.Series:
    hi, lo, c_prev = pd.Series(high).shift(1), pd.Series(low).shift(1),pd.Series(close).shift(1)
    tr = np.max([hi - lo, (c_prev - hi).abs(), (c_prev - lo).abs()], axis=0)
    atr = pd.Series(tr).rolling(n).mean().bfill()
    return atr

类似于tradingview，指标可以直接可视化显示出来，这个还是非常方便的。

简单的海龟策略的代码——这里仅实现了“突破”买入卖出：

from backtesting import Backtest, Strategy
from backtesting.lib import crossover

from backtesting.test import *


class TurtleStrategy(Strategy):
    def init(self):

        price = self.data.Close
        self.high = self.I(HIGH, price, 20)
        self.low = self.I(LOW, price, 10)
        self.atr = self.I(ATR, self.data.High, self.data.Low, price, 20)
        print(self.atr)

    def next(self):
        if self.data.Close[-1] > self.high[-1]:
            self.buy()
        if self.data.Close[-1] < self.low[-1]:
            self.sell()


bt = Backtest(GOOG, TurtleStrategy, commission=.002,
              exclusive_orders=True)
stats = bt.run()
print(stats)
bt.plot()

大家可以前往星球下载最新版本代码：

quantlab3.0系统代码预发布：附年化42%+的机器学习策略案例（小时级数据+代码下载）

原创文章第417篇，专注“AI量化投资、个人成长与财富自由“。

年化42.86%，比基准的12.86高出不少，回撤才9.43%，还是比较稳健的。

策略核心代码如下：我们使用KNN算法，大家可以考虑替换成SVM，或者树模型lightGBM。

class MLTrainOnceStrategy(Strategy):
    price_delta = .004  # 0.4%

    def init(self):
        # Init our model, a kNN classifier
        self.clf = KNeighborsClassifier(7)

        # Train the classifier in advance on the first N_TRAIN examples
        df = self.data.df.iloc[:N_TRAIN]
        X, y = get_clean_Xy(df)
        self.clf.fit(X, y)

        # Plot y for inspection
        self.I(get_y, self.data.df, name='y_true')

        # Prepare empty, all-NaN forecast indicator
        self.forecasts = self.I(lambda: np.repeat(np.nan, len(self.data)), name='forecast')

    def next(self):
        # Skip the training, in-sample data
        if len(self.data) < N_TRAIN:
            return

        # Proceed only with out-of-sample data. Prepare some variables
        high, low, close = self.data.High, self.data.Low, self.data.Close
        current_time = self.data.index[-1]

        # Forecast the next movement
        X = get_X(self.data.df.iloc[-1:])
        forecast = self.clf.predict(X)[0]

        # Update the plotted "forecast" indicator
        self.forecasts[-1] = forecast

        # If our forecast is upwards and we don't already hold a long position
        # place a long order for 20% of available account equity. Vice versa for short.
        # Also set target take-profit and stop-loss prices to be one price_delta
        # away from the current closing price.
        upper, lower = close[-1] * (1 + np.r_[1, -1] * self.price_delta)

        if forecast == 1 and not self.position.is_long:
            self.buy(size=.2, tp=upper, sl=lower)
        elif forecast == -1 and not self.position.is_short:
            self.sell(size=.2, tp=lower, sl=upper)

        # Additionally, set aggressive stop-loss on trades that have been open
        # for more than two days
        for trade in self.trades:
            if current_time - trade.entry_time > pd.Timedelta('2 days'):
                if trade.is_long:
                    trade.sl = max(trade.sl, low)
                else:
                    trade.sl = min(trade.sl, high)