97 lines
3.8 KiB
Python
97 lines
3.8 KiB
Python
from sklearn.preprocessing import MinMaxScaler
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import numpy as np
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def MakeFeatures(df):
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# Convert all F64 to F32 to save ram
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df = df.astype({col: 'float32' for col in df.select_dtypes('float64').columns})
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# Candle Wick's
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df['Spread'] = (df['High'] - df['Low']) / df['Close'] # in percentage of price
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candle_top = df[['Open', 'Close']].max(axis=1)
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df['Body_Size'] = (df['Close'] - df['Open']).abs() / (df['High'] - df['Low'])
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df['Upper_Shadow'] = (df['High'] - candle_top) / (df['High'] - df['Low'])
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# Is volume 2x higher than the 20-day average?
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df['Vol_Intensity'] = df['Volume'] / df['Volume'].shift(1).rolling(20).mean()
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# Volume Change
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df['Volume_Chg'] = df['Volume'].pct_change()
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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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# Add feature for Returns
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df['Return'] = df['Close'].pct_change()
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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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# Add feature for volitility last 5
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df['Volatility_5'] = df['Return'].transform(lambda x: x.shift(1).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.shift(1).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)).shift(1).rolling(window=14).mean()
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loss = (-delta.where(delta < 0, 0)).shift(1).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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# Return lagged
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for lag in range(1, 4):
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df[f'Return_Lag_{lag}'] = df['Return'].shift(lag)
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df[f'Vol_Lag_{lag}'] = df['Volume_Chg'].shift(lag)
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# This is our training metric of price difference 5 days ahead
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df['Target_Close'] = (np.log(df['Close'].shift(-5) / df['Close']) / df['Volatility_5']).clip(-10, 10)
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# Save the overall trend to predict based off of later
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with open("Target_Close_Average.txt", "w") as file:
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file.write(str(df["Target_Close"].mean()))
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# Drop every column that is a raw price or an unscaled average
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cols_to_drop = ['Open', 'High', 'Low', 'Volume', 'Close', 'Moving_Average_5', 'Moving_Average_20']
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for col in cols_to_drop:
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if col in df.columns:
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df.drop(col, axis=1, inplace=True)
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# Drop rows with null values
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df.dropna(inplace=True)
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# Replace Infinity with 0 -> This fixes the AI mental breakdown
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df = df.replace([np.inf, -np.inf], 0)
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# Replace Infinity with 0 -> This fixes the AI mental breakdown
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df['Volume_Chg'] = df['Volume_Chg'].replace([np.inf, -np.inf], 0)
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# Return new df with new features
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return df
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def Prepare(df):
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df = df.replace([np.inf, -np.inf], 0)
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# Remove indicators and set the target
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X = df.drop('Target_Close', axis=1)
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Y = df['Target_Close']
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# Scale the features to the same size
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feature_scaler = MinMaxScaler()
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X_scaled = feature_scaler.fit_transform(X)
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# Safe for the Y
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target_scaler = MinMaxScaler()
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y_scaled = target_scaler.fit_transform(Y.values.reshape(-1, 1))
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return X_scaled, y_scaled, feature_scaler, target_scaler
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