wael-lib 1.0.0

Technical Analysis, Data Handling & Sequence Utilities for Trading Applications

wael-lib

Technical Analysis, Data Handling & Sequence Utilities for Trading Applications.

Installation

pip install git+https://github.com/WaelFouda/wael-lib.git

Classes

TechAnalysis

30+ technical indicators and pattern detection:

• Moving Averages: SMA, EMA, WMA, TEMA
• Oscillators: RSI, Stochastic, MFI, ADX, MACD, TEMA MACD, Price Extension Oscillator
• Volatility: ATR, Standard Deviation
• Utilities: Highest, Lowest, Change, BarsSince, ValueWhen, Math helpers
• Pivot Points: Pivot High/Low with configurable left/right bars
• Trend Detection: Rolling window extremes, Uptrend/Downtrend/Sideways classification
• Structure Analysis: BOS (Break of Structure) & CHoCH (Change of Character) detection
• Chart Formations: Head & Shoulders, Double Top/Bottom, Triangles (continuation & reversal)
• Candlestick Patterns: 12 patterns (Engulfing, Hammer, Doji, Three Soldiers/Crows, etc.)

WDataHandler

• IQR-based outlier clipping
• Empirical Cumulative Distribution Function (ECDF)

SequenceCreator

• Sliding window sequence creation for LSTM/GRU models
• Supports univariate and multivariate time series
• Configurable window size and prediction horizon

SequenceScaler / MySequenceScaler

• sklearn-compatible transformers for 3D sequence data
• MinMaxScaler (SequenceScaler) and StandardScaler (MySequenceScaler) variants
• Pipeline-ready with fit() / transform() interface

Quick Start

from wael_lib import TechAnalysis, SequenceCreator, SequenceScaler

ta = TechAnalysis()

# Calculate indicators
rsi = ta.rsi(df['Close'], 14)
macd_line, signal, histogram = ta.macd(df['Close'])
trend = ta.detect_trend(df, row_index, tops, bottoms)
pattern = ta.detect_candlestick_pattern(df, row_index)

# Create sequences for LSTM
seq = SequenceCreator(window=30, horizon=1)
X_seq, y_seq = seq.create_sequences(X_features, y_target)

# Scale sequences
scaler = SequenceScaler()
X_scaled = scaler.fit_transform(X_seq)

Dependencies

• pandas >= 2.0.0
• numpy >= 1.24.0
• scikit-learn >= 1.3.0

License

MIT