{"id":34024226,"url":"https://github.com/implicitlayer/alphaanalysis","last_synced_at":"2026-03-12T15:01:43.821Z","repository":{"id":282574423,"uuid":"931196870","full_name":"ImplicitLayer/AlphaAnalysis","owner":"ImplicitLayer","description":"Full library for analyzing financial data using ML and classical approaches","archived":false,"fork":false,"pushed_at":"2025-04-12T09:21:07.000Z","size":112,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-12-15T05:34:20.924Z","etag":null,"topics":["data-analysis","data-science","deep-learning","finance","machine-learning","risk-analysis","time-series-analysis","trading","transformers"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ImplicitLayer.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-02-11T21:49:35.000Z","updated_at":"2025-04-16T22:34:28.000Z","dependencies_parsed_at":"2025-04-12T10:24:39.387Z","dependency_job_id":"c7b290c3-b2dc-4eff-b47b-a88fb9a58d5d","html_url":"https://github.com/ImplicitLayer/AlphaAnalysis","commit_stats":null,"previous_names":["implicitlayer/alphaanalysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ImplicitLayer/AlphaAnalysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImplicitLayer%2FAlphaAnalysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImplicitLayer%2FAlphaAnalysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImplicitLayer%2FAlphaAnalysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImplicitLayer%2FAlphaAnalysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ImplicitLayer","download_url":"https://codeload.github.com/ImplicitLayer/AlphaAnalysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ImplicitLayer%2FAlphaAnalysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30429292,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-12T14:34:45.044Z","status":"ssl_error","status_checked_at":"2026-03-12T14:09:33.793Z","response_time":114,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","deep-learning","finance","machine-learning","risk-analysis","time-series-analysis","trading","transformers"],"created_at":"2025-12-13T16:08:55.462Z","updated_at":"2026-03-12T15:01:43.807Z","avatar_url":"https://github.com/ImplicitLayer.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/user-attachments/assets/b1bf1d4b-03fb-4f46-99c9-92842feedec8\"\u003e\n\u003c/div\u003e\n\u003c/br\u003e\n\n**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.\n\n## Module Description\n\n### 1. generative_models\nModule for generate financial time series. This includes classes for modeling with GAN and VAE.\n\n### 2. modeling_financial_process\nA module for modeling financial data by stocasic precesses.\n\n### 3. models\nThis 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.\n\n### 4. trading\nModule for algorithmic trading. Includes functionality for backtesting trading strategies and risk management.\n\n### 5. portfolio\nModule for investment portfolio management. Includes tools for portfolio optimization, risk assessment and asset clustering.\n\n### 6. signal_generation\nModule for generating trading signals. Includes analysis of news and social networks (sentiment analysis), creation of technical signals and fundamental analysis.\n\n### 7. time_series\n\nModule, which contains methods for time series analysis\n\n## Installation\n\nUse pip to install the library:\n\n```bash\npip install alpha_analysis\n```\n\nOr follow these steps:\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/ImplicitLayer/AlphaAnalysis.git\ncd alpha_analysis\n```    \n2. Install the dependencies:\n```bash\npip install -r requirements.txt\n```\n3. For installing the library in your development environment, use:\n```bash\npip install -e .\n```\n\n## Examples of use\n\nExample of use for time series forecasting:\n\n```python\nfrom alpha_analysis.models import classical_models\nfrom alpha_analysis.data_preprocessing import data_loader\n\n# load data_preprocessing\ndata = data_loader.load_from_csv('historical_stock_data.csv')\n\n# using ARIMA model\nclassical_models = classical_models.ClassicalModels()\n\nforecast, summary = classical_models.arima_model(data, 'price', order=(5, 1, 0))\nprint(\"ARIMA Model Summary:\")\nprint(summary)\n```\n\nThe example of asset clustering:\n\n```python\nfrom alpha_analysis.portfolio.clustering import PortfolioClustering\nimport numpy as np\nimport pandas as pd\n\nnp.random.seed(42)\nassets = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA', 'NFLX', 'NVDA', 'JPM', 'XOM', 'META']\nnum_assets = len(assets)\nnum_days = 252\n\n# profit simulation\nsimulated_returns = pd.DataFrame(np.random.randn(num_days, num_assets) / 100, columns=assets)\n\nclustering = PortfolioClustering(simulated_returns)\n\n# K-Means\nkmeans_result = clustering.kmeans_clustering(num_clusters=3)\nprint(\"K-Means Clustering:\")\nprint(kmeans_result)\n```\n\n## Future plans\n\nWe are actively developing AlphaAnalysis and plan to add:\n\n* **Generative models (GANs)** to simulate market data\n* **Customisable trading strategies** via custom scripts\n* **Reinforcement learning**\n* More complex **modelling methods**\n    \nIf you have suggestions, open an issue in the repository!\n\n## Contacts\n\nWe can be contacted by:\n\n📩 Email: burenok023@gmail.com\n\n🔗 Telegram: @artemburenok\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimplicitlayer%2Falphaanalysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fimplicitlayer%2Falphaanalysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimplicitlayer%2Falphaanalysis/lists"}