Data transforms, features, and targets

Machine Learning for Finance in Python

Nathan George

Data Science Professor

Making features and targets

features = amd_df[['10d_close_pct', 'Adj_Volume']]

targets = amd_df['10d_future_close_pct']

print(type(features))

pandas.core.series.DataFrame

print(type(targets))

pandas.core.series.Series

price and moving average

Moving averages

Moving averages:

use n past days to get average
common values for n: 14, 50, 200

price and sma

RSI and price plot

RSI equation

RS equation

Calculating SMA and RSI

import talib

amd_df['ma200'] = talib.SMA(amd_df['Adj_Close'].values, timeperiod=200)

amd_df['rsi200'] = talib.RSI(amd_df['Adj_Close'].values, timeperiod=200)

Finally, our features

feature_names = ['10d_close_pct', 'ma200', 'rsi200']
features = amd_df[feature_names]
targets = amd_df['10d_future_close_pct']

feature_target_df = amd_df[feature_names + '10d_future_close_pct']

Check correlations

import seaborn as sns

corr = feature_target_df.corr()
sns.heatmap(corr, annot=True)

feature and target correlation plot

Let's create features and targets!

Machine Learning for Finance in Python