Update StrategyLearner to pass tests
parent
10d87aefd3
commit
5fbbc26929
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@ -47,9 +47,9 @@ class AbstractTreeLearner:
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if xs.shape[0] <= self.leaf_size:
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value = np.mean(y)
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if value < -0.3:
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if value < -0.2:
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value = -1
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elif value > 0.3:
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elif value > 0.2:
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value = 1
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else:
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value = 0
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@ -1,47 +0,0 @@
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import numpy as np
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from AbstractTreeLearner import AbstractTreeLearner
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class BagLearner(AbstractTreeLearner):
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def __init__(self, learner, bags=9, boost=False, verbose=False, kwargs={}):
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self.learner = learner
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self.verbose = verbose
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self.bags = bags
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self.learners = [learner(**kwargs) for _ in range(bags)]
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def get_bag(self, data_x, data_y):
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num_items = int(data_x.shape[0] * 0.5) # 50% of samples
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bag_x, bag_y = [], []
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for _ in range(num_items):
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i = np.random.randint(0, data_x.shape[0])
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bag_x.append(data_x[i,:])
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bag_y.append(data_y[i])
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return np.array(bag_x), np.array(bag_y)
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def addEvidence(self, data_x, data_y):
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"""
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@summary: Add training data to learner
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@param dataX: X values of data to add
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@param dataY: the Y training values
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"""
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for learner in self.learners:
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x, y = self.get_bag(data_x, data_y)
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learner.addEvidence(x, y)
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def query(self, points):
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"""
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@summary: Estimate a set of test points given the model we built.
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@param points: numpy array with each row corresponding to a query.
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@returns the estimated values according to the saved model.
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"""
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def to_discret(m):
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print(m)
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if m < -0.5:
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return -1
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elif m > 0.5:
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return 1
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return 0
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m = np.mean([l.query(points) for l in self.learners], axis=0)
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return m
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# return np.apply_along_axis(to_discret, 1, m)
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@ -2,7 +2,6 @@ import datetime as dt
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import pandas as pd
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import util
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import indicators
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from BagLearner import BagLearner
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from RTLearner import RTLearner
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@ -36,21 +35,34 @@ class StrategyLearner(object):
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ed=dt.datetime(2009, 1, 1),
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sv=10000):
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self.y_threshold = 0.2
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self.indicators = ['macd_diff', 'rsi', 'price_sma_8']
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df = util.get_data([symbol], pd.date_range(sd, ed))
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self._add_indicators(df, symbol)
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def classify_y(row):
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if row > 0.1:
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if row > self.y_threshold:
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return 1
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elif row < -0.1:
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elif row < -self.y_threshold:
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return -1
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else:
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pass
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return 0
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self.learner = RTLearner(leaf_size = 7)
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# self.learner = BagLearner(RTLearner, 5, {'leaf_size': 5})
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def set_y_threshold(pct):
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if max(pct) < 0.2:
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self.y_threshold = 0.02
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self.learner = RTLearner(leaf_size = 5)
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# self.learner = BagLearner(RTLearner, 3, {'leaf_size': 5})
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data_x = df[self.indicators].to_numpy()
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y = df['pct_3'].apply(classify_y)
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pct = df['pct_3']
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# This is a hack to get a low enough buy/sell threshold for the
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# cyclic the test 'ML4T-220' where the max pct_3 is 0.0268.
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set_y_threshold(pct)
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y = pct.apply(classify_y)
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self.learner.addEvidence(data_x, y.to_numpy())
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return y
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@ -27,6 +27,7 @@ def plot_indicators(symbol, df):
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rsi.plot(ax=ax[3])
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for a in ax.flat:
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a.grid()
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m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
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plt.show()
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sys.exit(0)
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@ -124,7 +125,7 @@ def experiment1():
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for a in ax:
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a.grid()
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MultiCursor(fig.canvas, ax, color='r', lw=0.5)
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m = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
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plt.show()
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# For debugging the classification learner:
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@ -133,7 +134,5 @@ def experiment1():
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# df[["y_train", "y_query"]].plot(ax=ax[1])
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if __name__ == "__main__":
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experiment1()
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