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This is a pandas-based technical analysis library and financial feature engineering tool. It serves as a vectorized indicator calculator that transforms raw price and volume data into derived metrics for time series analysis.

The library uses a NumPy-based engine to perform mathematical operations across entire arrays, avoiding iterative loops to maintain high performance. It organizes technical indicators into a modular class hierarchy with a consistent interface, allowing for bulk feature generation and the direct appending of results as new columns to a pandas DataFrame.

The system covers a wide range of financial metrics, including momentum oscillators, asset return metrics, and trend direction indicators. It also provides tools for measuring volatility through statistical bands and analyzing market pressure via volume-weighted metrics.

To ensure dataset completeness for machine learning, the processor includes configurable strategies for filling missing values and allows for the tuning of indicator parameters such as moving average periods.

Features

Feature Engineering Tools - Transforms raw financial market data into comprehensive sets of momentum, volatility, and trend features for trading models.

Technical Indicator Calculators - Implements a NumPy-based calculator for computing mathematical market metrics from historical price data.

Momentum Indicators - Calculates momentum oscillators to identify overbought or oversold market conditions and price velocity.

Volatility Indicators - Measures price variability using statistical bands and ranges to assess market risk and breakouts.

Time Series Feature Engineering - Transforms raw price and volume data into derived technical indicators for use in machine learning models.

Vectorized Array Operations - Performs mathematical operations across entire NumPy arrays simultaneously to eliminate iterative loops.

Pandas Financial Frameworks - Provides a pandas-based framework for computing financial indicators and aligning them as time series columns.

Price Return Calculators - Computes daily, cumulative, and logarithmic price returns to analyze asset performance.

Technical Indicator Engines - Implements a high-performance engine for calculating financial indicators through a modular class-based system.

Trend Direction Indicators - Identifies the direction and power of price movements using moving averages and trend clouds.

Pandas Vectorized Operations - Utilizes pandas vectorized operations to perform high-performance mathematical calculations across entire arrays.

Bulk Indicator Generation - Provides a single function to iterate over all indicator classes and bulk-append their outputs to a DataFrame.

Sequence Filling - Automatically fills NaN values introduced by indicator calculations using forward-fill or zero-fill strategies.

Dataframe Engines - Integrates computed indicators directly into pandas DataFrame structures while preserving time series alignment.

DataFrame Integration - Appends computed indicator results as new columns to a pandas DataFrame to maintain time series alignment.

Missing Value Imputation - Provides configurable forward-fill and zero-fill strategies to handle calculation gaps in financial datasets.

Financial Time-Series Cleaners - Processes financial time series by computing asset returns and filling missing values to ensure data integrity.

Volume Flow Indicators - Provides flow-based metrics that combine price and volume to identify market buying and selling pressure.

Interface-Based Aggregators - Iterates through indicator classes to bulk-generate features using a single standardized function call.

Technical Indicators - Technical analysis library using Pandas.

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Ta

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