https://github.com/implicitlayer/alphaanalysis
Full library for analyzing financial data using ML and classical approaches
https://github.com/implicitlayer/alphaanalysis
data-analysis data-science deep-learning finance machine-learning risk-analysis time-series-analysis trading transformers
Last synced: 3 months ago
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Full library for analyzing financial data using ML and classical approaches
- Host: GitHub
- URL: https://github.com/implicitlayer/alphaanalysis
- Owner: ImplicitLayer
- License: apache-2.0
- Created: 2025-02-11T21:49:35.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-12T09:21:07.000Z (about 1 year ago)
- Last Synced: 2025-12-15T05:34:20.924Z (6 months ago)
- Topics: data-analysis, data-science, deep-learning, finance, machine-learning, risk-analysis, time-series-analysis, trading, transformers
- Language: Python
- Homepage:
- Size: 109 KB
- Stars: 3
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**AlphaAnalysis** — is a library for analyzing financial indicators, which includes classical data analysis methods and machine learning. The project is intended for use in financial markets and can be useful for analysis, forecasting and optimization of trading strategies, as well as for portfolio management.
## Module Description
### 1. generative_models
Module for generate financial time series. This includes classes for modeling with GAN and VAE.
### 2. modeling_financial_process
A module for modeling financial data by stocasic precesses.
### 3. models
This module includes various models for analyzing financial data, both classical (e.g. ARIMA, GARCH, VAR) and modern machine learning models (Random Forest, XGBoost, CatBoost), as well as deep neural networks (LSTM, GRU, Transformer) for trading.
### 4. trading
Module for algorithmic trading. Includes functionality for backtesting trading strategies and risk management.
### 5. portfolio
Module for investment portfolio management. Includes tools for portfolio optimization, risk assessment and asset clustering.
### 6. signal_generation
Module for generating trading signals. Includes analysis of news and social networks (sentiment analysis), creation of technical signals and fundamental analysis.
### 7. time_series
Module, which contains methods for time series analysis
## Installation
Use pip to install the library:
```bash
pip install alpha_analysis
```
Or follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/ImplicitLayer/AlphaAnalysis.git
cd alpha_analysis
```
2. Install the dependencies:
```bash
pip install -r requirements.txt
```
3. For installing the library in your development environment, use:
```bash
pip install -e .
```
## Examples of use
Example of use for time series forecasting:
```python
from alpha_analysis.models import classical_models
from alpha_analysis.data_preprocessing import data_loader
# load data_preprocessing
data = data_loader.load_from_csv('historical_stock_data.csv')
# using ARIMA model
classical_models = classical_models.ClassicalModels()
forecast, summary = classical_models.arima_model(data, 'price', order=(5, 1, 0))
print("ARIMA Model Summary:")
print(summary)
```
The example of asset clustering:
```python
from alpha_analysis.portfolio.clustering import PortfolioClustering
import numpy as np
import pandas as pd
np.random.seed(42)
assets = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA', 'NFLX', 'NVDA', 'JPM', 'XOM', 'META']
num_assets = len(assets)
num_days = 252
# profit simulation
simulated_returns = pd.DataFrame(np.random.randn(num_days, num_assets) / 100, columns=assets)
clustering = PortfolioClustering(simulated_returns)
# K-Means
kmeans_result = clustering.kmeans_clustering(num_clusters=3)
print("K-Means Clustering:")
print(kmeans_result)
```
## Future plans
We are actively developing AlphaAnalysis and plan to add:
* **Generative models (GANs)** to simulate market data
* **Customisable trading strategies** via custom scripts
* **Reinforcement learning**
* More complex **modelling methods**
If you have suggestions, open an issue in the repository!
## Contacts
We can be contacted by:
📩 Email: burenok023@gmail.com
🔗 Telegram: @artemburenok