mha-finance 0.15.2

Multi-Horizon Statistical Modeling for Financial Time Series

mha-finance

Multi-Horizon Statistical Modeling for Financial Time Series

A Python framework for descriptive statistical analysis of financial assets across multiple time horizons.

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Overview

mha-finance is a Python framework for multi-horizon statistical modeling of financial time series.

It is designed to help users characterize, not predict, the behavior of financial assets across different horizons such as weekly, monthly, and annual windows.

The library focuses on three core analytical tasks:

• Return characterization
• Volatility characterization
• Market regime characterization

Instead of forecasting prices or generating trading signals, mha-finance provides descriptive and inferential statistical summaries built from historical market data.

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View Documentation at : https://vkverma9534.github.io/mha-finance/

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Installation

Install directly from PyPI:

pip install mha-finance

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Quick Start

mha-finance provides a Trigger API for quick first-time usage.

These are the easiest entry points into the library.

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1) Return Characterization

Estimate horizon-wise return statistics for a symbol.

from mha.Trigger.returns import returns_trigger

result = returns_trigger(symbol="GS", horizon="M", lookback=5)
print(result)

Example Output

{
    'symbol': 'GS',
    'horizon': 'Monthly',
    'mean_returns_pct': 1.6549,
    'median_returns_pct': 1.0414,
    'time_weighted_mean_returns_pct': 2.3761,
    'dispersion_pct': 0.6611
}

What it gives you

• mean realized return
• median realized return
• time-weighted average return
• return dispersion

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2) Volatility Characterization

Estimate recent conditional variability and uncertainty.

from mha.Trigger.volatility import volatility_trigger

result = volatility_trigger(
    symbol="GS",
    horizon="M",
    lookback=5,
    decay_parameter=0.985
)

print(result)

Example Output

{
    'symbol': 'GS',
    'horizon': 'M',
    'volatility': {
        'raw': 0.08059,
        'percent': 8.0593
    },
    'time_weighted_volatility': {
        'raw': 0.08433,
        'percent': 8.4326
    },
    'volatility_uncertainty': {
        'raw': 0.0001727,
        'percent': 0.01727
    },
    'relative_volatility_change': {
        'percent': 0.3194,
        'flag': 'Smooth (Safe)'
    }
}

What it gives you

• estimated volatility
• time-weighted volatility
• uncertainty in volatility estimate
• relative change / stability signal

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3) Market Regime Characterization

Identify statistically similar return-volatility environments over time.

from mha.Trigger.regime import regime_trigger

result = regime_trigger(symbol="GS", horizon="M", lookback=5)
print(result)

Example Output

{
    'symbol': 'GS',
    'horizon': 'M',
    'lookback': 5,
    'window_length': 11,
    'n_regimes': 5,
    'regime_by_time': {
        ...
    }
}

What it gives you

• number of identified regimes
• regime labels through time
• horizon-wise regime segmentation

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Example: Plot Regime Timeline

import pandas as pd
import matplotlib.pyplot as plt

from mha.Trigger.regime import regime_trigger

# Run trigger
result = regime_trigger(symbol="GS", horizon="M", lookback=5)

data = result if isinstance(result, dict) else result.__dict__

df = pd.DataFrame(list(data["regime_by_time"].items()), columns=["date", "regime"])
df["date"] = pd.to_datetime(df["date"])
df = df.sort_values("date")

plt.figure(figsize=(14, 4))
plt.step(df["date"], df["regime"], where="post", linewidth=2)
plt.scatter(df["date"], df["regime"], s=40)

plt.title(f"{data['symbol']} Regime Timeline", fontsize=14)
plt.xlabel("Date")
plt.ylabel("Regime")
plt.yticks(range(data["n_regimes"]))
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

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4) Ticker Search Utility (Available for use in the newest version-(0.15.0)):

mha-finance now includes a lightweight ticker discovery utility for resolving company or asset names into Yahoo Finance ticker symbols.

from mha.data.explore import TickerSearch

results = TickerSearch("Goldman Sachs")

print(results)

Example Output

[
    {
        "name": "Goldman Sachs Group Inc.",
        "ticker": "GS"
    }
]

TickerSearch helps users discover valid ticker symbols by searching Yahoo Finance for matching companies or organizations. It is useful for quick asset lookup, exploratory analysis, and automated financial workflows.

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Design Philosophy

mha-finance is built around a simple idea:

Financial behavior changes across time horizons, and each horizon should be studied statistically on its own terms.

This package is designed for:

• statistical exploration
• reproducible financial analysis
• academic / educational use
• horizon-aware market characterization

This package is not intended for:

• price prediction
• market timing
• trading signals
• portfolio optimization
• investment advice

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API Structure

mha-finance is organized into two usage layers:

1) Trigger API (Recommended for most users)

High-level entry points for fast analysis:

• returns_trigger(...)
• volatility_trigger(...)
• regime_trigger(...)

Use this layer when you want quick statistical summaries with minimal setup.

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2) Base Analytical Functions

Under the trigger layer, the library contains several lower-level analytical functions that can be used independently for:

• custom workflows
• experimentation
• modular research pipelines
• advanced extension and development

This makes mha-finance suitable for both:

• beginners who want quick outputs
• advanced users who want analytical control

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Core Objectives

The framework is built around three statistical objectives:

1. Horizon-wise Return Characterization

Estimate and summarize realized returns over a selected horizon using historical price data.

Includes:

• horizon-based log returns
• rolling estimation windows
• mean / median return statistics
• dispersion and uncertainty analysis

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2. Horizon-wise Volatility Characterization

Estimate recent conditional variability of returns without forecasting future volatility.

Includes:

• rolling variance / standard deviation
• EWMA-based estimation
• stability and uncertainty diagnostics
• horizon-aware volatility summaries

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3. Horizon-wise Market Regime Characterization

Identify recurring statistical states of the market based on return-volatility structure.

Includes:

• statistical feature construction
• unsupervised regime partitioning
• regime labels across time
• descriptive market state summaries

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Data Source

mha-finance retrieves historical market data at runtime using Yahoo Finance through the yfinance Python library.

Data Handling Policy

The framework follows a non-redistributive data usage model:

• No raw OHLCV or intraday datasets are bundled with the package
• No historical market datasets are shipped in the repository
• No price data is redistributed by this project
• Raw data is used only during runtime for statistical computation

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Important Disclaimer

mha-finance is an analytical and educational framework.

It is intended for:

• statistical analysis
• research workflows
• exploratory finance studies

It is not intended to provide:

• investment advice
• buy/sell recommendations
• trading signals
• guaranteed market insight

All outputs should be interpreted as descriptive statistical summaries, not predictive financial guidance.

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Why This Library Exists

Most financial tooling is built around one of two extremes:

• very basic charting and indicators
• fully predictive / trading-oriented pipelines

mha-finance is built for the space in between:

serious descriptive statistical analysis of financial time series across multiple horizons

It aims to provide a cleaner foundation for users who want to study market behavior rigorously before jumping into forecasting or decision systems.

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Roadmap

Planned and possible future directions include:

• richer uncertainty summaries
• extended horizon support
• improved regime diagnostics
• enhanced visualization utilities
• more modular lower-level statistical tools

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