def get_features(self, prices):
"""
Compute the technical features of a position and feed that
into the Q-Learning process
TODO: Try different parameters to see if you can improve
the portfolio
Try out different window sizes (right now set to 10 trading days)
prices: Adjusted close prices of the given symbol
df_features: A pandas dataframe of the technical indicators
"""
rolling_mean = prices.rolling(window=self.window_size).mean()
rolling_std = prices.rolling(window=self.window_size).std()
momentum = get_momentum(prices, self.window_size)
sma_indicator = get_sma_indicator(prices, rolling_mean)
bollinger_val = compute_bollinger_value(prices, rolling_mean, rolling_std)
df_features = pd.concat([momentum, sma_indicator], axis=1)
df_features = pd.concat([df_features, bollinger_val], axis=1)
df_features.columns = ["ind{}".format(i)
for i in range(len(df_features.columns))]
# Perhaps you can come up with a different feature?
# Try it!
df_features.dropna(inplace=True)
return df_features
def get_features(self, prices):
"""Compute technical indicators and use them as features to be fed
into a Q-learner.
Parameters:
prices: Adjusted close prices of the given symbol
Returns:
df_features: A pandas dataframe of the technical indicators
"""
window = 10
# Compute rolling mean
rolling_mean = prices.rolling(window=window).mean()
# Compute_rolling_std
rolling_std = prices.rolling(window=window).std()
# Compute momentum
momentum = get_momentum(prices, window)
# Compute SMA indicator
sma_indicator = get_sma_indicator(prices, rolling_mean)
# Compute Bollinger value
bollinger_val = compute_bollinger_value(prices, rolling_mean, rolling_std)
# Create a dataframe with three features
df_features = pd.concat([momentum, sma_indicator], axis=1)
df_features = pd.concat([df_features, bollinger_val], axis=1)
df_features.columns = ["ind{}".format(i)
for i in range(len(df_features.columns))]
df_features.dropna(inplace=True)
return df_features