if prob > 0.6 and position == 0: # Buy position = capital / current_price capital = 0 elif prob < 0.4 and position > 0: # Sell capital = position * current_price position = 0
Buy when a short-term moving average crosses above a long-term moving average (Golden Cross).
Algorithmic trading removes human emotion from financial markets. Systems execute buy and sell orders based on mathematical formulas, timing, price, or quantity constraints. Core Components
Ensure the date is set as the index for time-series analysis. 4. Building Technical Analysis Strategies
capital = 100000 position = 0 equity_curve = []
While technical indicators are the standard starting point, robust alpha factors can be constructed from many other data sources: sentiment analysis of real-time news feeds, fundamental data for factor investing, or on-chain metrics for cryptocurrency strategies. The FinML-Toolkit library provides a structured pipeline for financial feature engineering with support for imbalanced data handling via SMOTE, ADASYN, and other techniques.
Raw prices are useless for predictions. You must create features. For a machine learning model to predict future price movement, calculate:
offers a complete toolkit for feature engineering, model development, and portfolio analysis. It includes feature creation, transformation, and selection capabilities.
data_clean = data.dropna()
| Category | Recommended Libraries | | --- | --- | | | Pandas, NumPy, Polars | | Indicators | TA-Lib, pandas-ta | | ML Models | scikit-learn, XGBoost, LightGBM, TensorFlow/Keras | | Backtesting | VectorBT, Zipline Refresh, Backtrader | | Live Trading | Alpaca API, Interactive Brokers, OlympusTrader | | Risk Mgmt | Custom ATR/Kelly implementations |
: Stop price moves upward as the trade becomes profitable, locking in gains while allowing upside.
A strategy that looks great on close-to-close backtests often fails due to slippage — the difference between expected and actual fill price. For strategies trading at high frequencies, slippage can be the dominant cost. The honest-bt library explicitly addresses backtesting bugs that inflate Sharpe ratios, including correct commission modeling and realistic fill simulation.
Don't risk 100% on one trade. Use the : f* = (p * b - q) / b Where p = win probability, b = avg win/avg loss.
# Forward-looking target (no look-ahead in production!) df['future_return'] = df['Close'].shift(-5) / df['Close'] - 1 df['target'] = (df['future_return'] > 0).astype(int)
if prob > 0.6 and position == 0: # Buy position = capital / current_price capital = 0 elif prob < 0.4 and position > 0: # Sell capital = position * current_price position = 0
Buy when a short-term moving average crosses above a long-term moving average (Golden Cross).
Algorithmic trading removes human emotion from financial markets. Systems execute buy and sell orders based on mathematical formulas, timing, price, or quantity constraints. Core Components
Ensure the date is set as the index for time-series analysis. 4. Building Technical Analysis Strategies
capital = 100000 position = 0 equity_curve = []
While technical indicators are the standard starting point, robust alpha factors can be constructed from many other data sources: sentiment analysis of real-time news feeds, fundamental data for factor investing, or on-chain metrics for cryptocurrency strategies. The FinML-Toolkit library provides a structured pipeline for financial feature engineering with support for imbalanced data handling via SMOTE, ADASYN, and other techniques.
Raw prices are useless for predictions. You must create features. For a machine learning model to predict future price movement, calculate:
offers a complete toolkit for feature engineering, model development, and portfolio analysis. It includes feature creation, transformation, and selection capabilities.
data_clean = data.dropna()
| Category | Recommended Libraries | | --- | --- | | | Pandas, NumPy, Polars | | Indicators | TA-Lib, pandas-ta | | ML Models | scikit-learn, XGBoost, LightGBM, TensorFlow/Keras | | Backtesting | VectorBT, Zipline Refresh, Backtrader | | Live Trading | Alpaca API, Interactive Brokers, OlympusTrader | | Risk Mgmt | Custom ATR/Kelly implementations |
: Stop price moves upward as the trade becomes profitable, locking in gains while allowing upside.
A strategy that looks great on close-to-close backtests often fails due to slippage — the difference between expected and actual fill price. For strategies trading at high frequencies, slippage can be the dominant cost. The honest-bt library explicitly addresses backtesting bugs that inflate Sharpe ratios, including correct commission modeling and realistic fill simulation.
Don't risk 100% on one trade. Use the : f* = (p * b - q) / b Where p = win probability, b = avg win/avg loss.
# Forward-looking target (no look-ahead in production!) df['future_return'] = df['Close'].shift(-5) / df['Close'] - 1 df['target'] = (df['future_return'] > 0).astype(int)
