derek holloway
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
import pandas as pd
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import numpy as np
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def MakeFeatures(df, i):
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# Remove the ticker column
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df.columns = df.columns.get_level_values(0)
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# Make sure Date is a number object
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df = df.reset_index()
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df['Date'] = pd.to_numeric(pd.to_datetime(df['Date']))
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# Add the Symbol column for tracking | as an int 1 hot encoded
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df['Symbol'] = i
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# Add feature Spread
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df['Spread'] = abs( df['High'] - df['Low'] )
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# Add feature for Returns
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df['Return'] = df['Close'].pct_change()
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# Add feature for volitility last 5
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df['Volatility_5'] = df['Return'].transform(lambda x: x.rolling(5).std())
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# Add feature for volitility last 20
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df['Volatility_20'] = df['Return'].transform(lambda x: x.rolling(20).std())
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# RSI (Relative Strength Index)
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delta = df['Close'].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
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rs = gain / loss
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df['RSI'] = 100 - (100 / (1 + rs))
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# Moving Average Crossover (Golden/Death Cross logic)
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df['Moving_Average_5'] = df['Close'].rolling(window=5).mean()
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df['Moving_Average_20'] = df['Close'].rolling(window=20).mean()
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# if short term > long term (bullish), else 0
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df['Trend_Signal'] = (df['Moving_Average_5'] > df['Moving_Average_20']).astype(int)
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# Distance from MA (How overextended are we?)
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df['Dist_From_MA20'] = (df['Close'] / df['Moving_Average_20']) - 1
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# Bollinger Band Position (Where are we relative to volatility?)
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std_20 = df['Close'].rolling(20).std()
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upper_band = df['Moving_Average_20'] + (std_20 * 2)
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lower_band = df['Moving_Average_20'] - (std_20 * 2)
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df['BB_Pos'] = (df['Close'] - lower_band) / (upper_band - lower_band)
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# Log Returns (Better for AI than pct_change for statistical normality)
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df['Log_Return'] = np.log(df['Close'] / df['Close'].shift(1))
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# This is our training metric of 5 days ahead
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df['Target_Close'] = df['Close'].shift(-5).pct_change()
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# for Up (> 0.5%), -1 for Down (< -0.5%), 0 for Flat
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df['Target_Direction'] = np.where(df['Target_Close'] > 0.005, 1, np.where(df['Target_Close'] < -0.005, -1, 0))
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# Volume Change
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df['Volume_Chg'] = df['Volume'].pct_change()
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# Return new df with new features
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return df
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def CleanDF(df):
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# Make date the index so it doesnt influence the training
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df.set_index('Date', inplace=True)
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# Drop rows with null values
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df.dropna(inplace=True)
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# Return new df thats cleaned
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return df
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