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\n \"Tauric\n

\n\n# TradingAgents: Multi-Agents LLM Financial Trading Framework\n\n
\n \"arXiv\"\n \"Discord\"\n \"X\n \"Community\"\n
\n\n
\n
\n \"TradingAgents\n
\n\n## Latest: [Trading-R1](https://github.com/TauricResearch/Trading-R1) is now in early access\n\nAfter so many requests from the community, Trading-R1 has officially launched!\n\n

\n \"Trading-R1\n

\n\n

Register for early access ↗

\n\n## News\n\n\n- [2026-11] **Trading-R1 v0.1.0** released, and [early access](https://github.com/TauricResearch/Trading-R1) is open.\n- [2026-10] **TradingAgents v0.7.1** released with decisions scored only on final closes and settled across long market holidays, a fix for model dates that could read filings made after the run date, and fixes to portfolios, prices, SEC statements and reports.\n- [2026-10] **TradingAgents v0.7.0** released with runs on your own ChatGPT Plus or Pro plan, backtests scored from the first close after each decision, a note at start-up when a newer release is out, and a wheel attached to each GitHub release.\n\n
\nEarlier news\n\n- [2026-10] **TradingAgents v0.6.0** released with reports saved as one HTML page, a provider per model tier so the managers and analysts can run on different models, past decisions settled for every ticker while the analysts work, and company news read from Yahoo search while Yahoo's news feed is down.\n- [2026-09] **TradingAgents v0.5.2** released with parallel analysts for a faster analysis, a CLI that runs without prompts from flags such as `--ticker` and `--date`, the run's settings recorded in every report, and backtests that see only data published by each analysis date.\n- [2026-09] **TradingAgents v0.5.1** released with a package layout organised by what each module holds (import paths moved), optional Jev screening of social posts, GPT-6 Sol and Luna as the default models, and fixes to run isolation and SEC EDGAR statements.\n- [2026-09] **TradingAgents v0.5.0** released with point-in-time integrity across every dated path, SEC EDGAR fundamentals served as filed, backtesting over a ticker and date grid, portfolio-aware runs, and current model lineups across every provider.\n- [2026-08] **TradingAgents v0.4.0** released with look-ahead / point-in-time fixes across FRED macro, social sentiment, and the decision-log memory; clearer decision signals; working CLI checkpoint resume; Trader price grounding; and the GPT-5.6 and GLM-5.3 models.\n- [2026-07] **TradingAgents v0.3.1** released with correctness and stability fixes: Alpha Vantage look-ahead filtering, graph-router crash-safety, graph-shape-aware checkpoint resume, working crypto sentiment sources, a configurable LLM retry budget, Bedrock API-key auth, and Claude Sonnet 5 / Fable 5 support.\n- [2026-06] **TradingAgents v0.3.0** released with a verified data-access contract, an expanded provider registry (NVIDIA, Kimi, Groq, Mistral, Bedrock, and any OpenAI-compatible endpoint), FRED and Polymarket data vendors, a current-generation model catalog, and a CI gate.\n- [2026-05] **TradingAgents v0.2.5** released with the grounded Sentiment Analyst, GPT-5.5 etc. model coverage, Qwen/GLM/MiniMax dual-region support, `TRADINGAGENTS_*` env-var configurability with API-key auto-detection, remote Ollama support, non-US alpha benchmarks, and ticker path-traversal hardening.\n- [2026-04] **TradingAgents v0.2.4** released with structured-output agents (Research Manager, Trader, Portfolio Manager), LangGraph checkpoint resume, persistent decision log, DeepSeek/Qwen/GLM/Azure provider support, Docker, and a Windows UTF-8 encoding fix.\n- [2026-03] **TradingAgents v0.2.3** released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.\n- [2026-03] **TradingAgents v0.2.2** released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.\n- [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.\n- [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon.\n\n
\n\n\n
\n\n🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)\n\n
\n\n> 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community.\n>\n> So we decided to fully open-source the framework. Looking forward to building impactful projects with you!\n\n## TradingAgents Framework\n\nTradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.\n\n

\n \n

\n\n> TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/)\n\nOur framework decomposes complex trading tasks into specialized roles.\n\n### Analyst Team\n- Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags.\n- Sentiment Analyst: Aggregates news headlines, StockTwits, and Reddit chatter into a single sentiment read to gauge short-term market mood.\n- News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions.\n- Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.\n\nThe selected analysts work at the same time, each on its own tools, and the research debate starts once all of their reports are in.\n\n

\n \n

\n\n### Researcher Team\n- Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks.\n\n

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\n\n### Trader Agent\n- Composes reports from the analysts and researchers to make informed trading decisions, determining the timing and magnitude of trades.\n\n

\n \n

\n\n### Risk Management and Portfolio Manager\n- Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision.\n- The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.\n\n

\n \n

\n\n## Installation and CLI\n\n### Installation\n\nClone TradingAgents:\n```bash\ngit clone https://github.com/TauricResearch/TradingAgents.git\ncd TradingAgents\n```\n\nTradingAgents needs Python 3.11 or later. Create a virtual environment in any of your favorite environment managers:\n```bash\nconda create -n tradingagents python=3.13\nconda activate tradingagents\n```\n\nOr with [uv](https://docs.astral.sh/uv/):\n```bash\nuv venv --python 3.13\nsource .venv/bin/activate\n```\n\nInstall the package and its dependencies (`uv pip install .` with uv):\n```bash\npip install .\n```\n\n### Docker\n\nAlternatively, run with Docker:\n```bash\ncp .env.example .env # add your API keys\ndocker compose run --rm tradingagents\n```\n\nAfter updating the repository, rebuild the image with `docker compose build`.\n\nResults, reports, the memory log and the cache live in the `tradingagents_data` volume. To keep them in a folder on the host instead, create the folder and point `TRADINGAGENTS_DATA_DIR` at it, in `.env` or the shell: `mkdir -p data && TRADINGAGENTS_DATA_DIR=./data docker compose run --rm tradingagents`.\n\nFor local models with Ollama:\n```bash\ndocker compose --profile ollama run --rm tradingagents-ollama\n```\n\n### Required APIs\n\nTradingAgents supports multiple LLM providers. Set the API key for your chosen provider:\n\n```bash\nexport OPENAI_API_KEY=... # OpenAI (GPT)\nexport GOOGLE_API_KEY=... # Google (Gemini)\nexport ANTHROPIC_API_KEY=... # Anthropic (Claude)\nexport XAI_API_KEY=... # xAI (Grok)\nexport DEEPSEEK_API_KEY=... # DeepSeek\nexport DASHSCOPE_API_KEY=... # Qwen (international, dashscope-intl.aliyuncs.com)\nexport DASHSCOPE_CN_API_KEY=... # Qwen (China, dashscope.aliyuncs.com)\nexport ZHIPU_API_KEY=... # GLM via Z.AI (international)\nexport ZHIPU_CN_API_KEY=... # GLM via BigModel (China, open.bigmodel.cn)\nexport MINIMAX_API_KEY=... # MiniMax (global, api.minimax.io)\nexport MINIMAX_CN_API_KEY=... # MiniMax (China, api.minimaxi.com)\nexport OPENROUTER_API_KEY=... # OpenRouter\nexport MISTRAL_API_KEY=... # Mistral\nexport MOONSHOT_API_KEY=... # Kimi (Moonshot)\nexport GROQ_API_KEY=... # Groq\nexport NVIDIA_API_KEY=... # NVIDIA NIM\nexport FRED_API_KEY=... # FRED macro data (free, optional)\nexport ALPHA_VANTAGE_API_KEY=... # Alpha Vantage\nexport TYPESAFE_API_KEY=... # Jev social-post screening (optional)\n```\n\nFor Azure OpenAI, copy `.env.enterprise.example` to `.env.enterprise` and fill in your credentials. With Docker, put these keys in `.env` instead: the image does not include `.env` files.\n\nTo run on your ChatGPT Plus or Pro plan instead of an API key, choose \"Subscription\" in the CLI's provider menu, or set `llm_provider: \"chatgpt\"`. Sign in once with `tradingagents login chatgpt`: it prints an address to open in your browser, which returns to this machine on `127.0.0.1`. When the browser runs on another machine, or TradingAgents runs in Docker (`docker compose run --rm tradingagents login chatgpt`), paste the address the browser ends up on. The sign-in is kept in `~/.tradingagents/chatgpt-auth.json`, and `tradingagents logout chatgpt` revokes it. Runs count toward your plan's usage limits, and the plan does not accept `temperature` or `max_tokens`.\n\nFor AWS Bedrock, install the extra with `pip install \".[bedrock]\"`, set `llm_provider: \"bedrock\"`, configure AWS credentials (environment variables, `~/.aws/credentials`, or an IAM role) and `AWS_DEFAULT_REGION`, and use a Bedrock model ID, e.g. `us.anthropic.claude-opus-5-5`.\n\nFor local models, configure Ollama with `llm_provider: \"ollama\"`. The default endpoint is `http://localhost:11434/v1`; set `OLLAMA_BASE_URL` to point at a remote `ollama-serve`. Pull models with `ollama pull `, and pick \"Custom model ID\" in the CLI for any model not listed by default.\n\nFor any other OpenAI-compatible server (vLLM, LM Studio, llama.cpp, or a custom relay), use `llm_provider: \"openai_compatible\"` and set the endpoint via `backend_url` (or `TRADINGAGENTS_LLM_BACKEND_URL`), e.g. `http://localhost:8000/v1` for vLLM or `http://localhost:1234/v1` for LM Studio. The model is whatever your server serves. No key is needed for local servers; set `OPENAI_COMPATIBLE_API_KEY` when the endpoint requires one.\n\nWith `TYPESAFE_API_KEY` set, the Sentiment Analyst screens StockTwits and Reddit posts with TypeSafe's Jev before reading them. Posts that are not about the company are dropped, and each source opens with a count of the remaining posts by stance: bullish, bearish, neutral, or unclear. Without the key, posts pass through unscreened. `jev-latest` moves with new releases; set `TYPESAFE_DEFAULT_MODEL` to a versioned ID such as `jev-1.13.0` to hold it fixed across runs. To reach Jev through OpenRouter, put an OpenRouter key in `TYPESAFE_API_KEY` and set `TYPESAFE_BASE_URL=https://openrouter.ai/api`.\n\nAlternatively, copy `.env.example` to `.env` and fill in your keys:\n```bash\ncp .env.example .env\n```\n\n### CLI Usage\n\nLaunch the interactive CLI:\n```bash\ntradingagents # installed command\npython -m cli.main # alternative: run directly from source\n```\nYou will see a screen where you can select your desired tickers, analysis date, LLM provider, research depth, and more. Your previous run's answers come back as the defaults, so pressing Enter accepts them. The `TRADINGAGENTS_*` variables in `.env` still skip their step entirely.\n\nTo run without questions, for a scheduled job or a script, answer the per-run steps with flags and the rest with `TRADINGAGENTS_*` variables:\n```bash\nexport TRADINGAGENTS_LLM_PROVIDER=openai TRADINGAGENTS_QUICK_THINK_LLM=gpt-6-luna TRADINGAGENTS_DEEP_THINK_LLM=gpt-6-sol\nexport TRADINGAGENTS_OUTPUT_LANGUAGE=English TRADINGAGENTS_MAX_DEBATE_ROUNDS=1 TRADINGAGENTS_MAX_RISK_ROUNDS=1\ntradingagents --ticker NVDA --date 2026-09-23 --analysts market,news,fundamentals --save --no-show\n```\nEach flag skips only its own question. Run without a terminal, a missing answer stops the run before it starts and names the flag or variable to set.\n\nAt start-up the interactive CLI mentions a newer release and notices from Tauric Research. It checks at most once a day, in the background, and sends nothing about your runs; set `TRADINGAGENTS_NO_UPDATE_CHECK=1` to turn it off. Runs without a terminal never check.\n\nA saved report also includes `complete_report.html`, the report as one page with its sections listed beside the text, for reading in a browser, on a phone or in print. Answering the save question at the prompt also asks about the page and can open it in your browser; `--no-html` skips it, and so does `ta.save_reports(state, \"NVDA\", html=False)` from Python.\n\n### Markets and tickers\n\nTradingAgents works with any market Yahoo Finance covers, using the exchange-suffixed ticker. Company identity and the alpha benchmark resolve automatically per market.\n\n- US: `AAPL`, `SPY`\n- Hong Kong: `0700.HK` · Tokyo: `7203.T` · London: `AZN.L`\n- India: `RELIANCE.NS`, `.BO` · Canada: `.TO` · Australia: `.AX`\n- China A-shares: Shanghai `.SS`, Shenzhen `.SZ` (e.g. `600519.SS` for Kweichow Moutai)\n- Crypto: `BTC-USD`, `ETH-USD`\n\n

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\n\nAn interface will appear showing results as they load, letting you track the agent's progress as it runs.\n\n

\n \n

\n\n

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\n\n## TradingAgents Package\n\n### Implementation Details\n\nWe built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen (Alibaba DashScope, international and China endpoints), GLM (Zhipu), MiniMax (global + China), OpenRouter, Ollama for local models, Azure OpenAI for enterprise, and your own ChatGPT plan.\n\n### Python Usage\n\nTo use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example:\n\n```python\nfrom tradingagents.graph.trading_graph import TradingAgentsGraph\nfrom tradingagents.default_config import DEFAULT_CONFIG\n\nta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())\n\n# forward propagate\nstate, decision = ta.propagate(\"NVDA\", \"2026-09-01\")\nprint(decision)\n\n# the same report tree the CLI saves, under results_dir/reports\nta.save_reports(state, \"NVDA\")\n```\n\nYou can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.\n\n```python\nfrom tradingagents.graph.trading_graph import TradingAgentsGraph\nfrom tradingagents.default_config import DEFAULT_CONFIG\n\nconfig = DEFAULT_CONFIG.copy()\nconfig[\"llm_provider\"] = \"openai\" # e.g. openai, google, anthropic, deepseek, groq, ollama; openai_compatible covers any OpenAI-compatible endpoint (vLLM, LM Studio, llama.cpp, ...)\nconfig[\"deep_think_llm\"] = \"gpt-6-sol\" # Model for complex reasoning\nconfig[\"quick_think_llm\"] = \"gpt-6-luna\" # Model for quick tasks\nconfig[\"max_debate_rounds\"] = 2\n\nta = TradingAgentsGraph(debug=True, config=config)\n_, decision = ta.propagate(\"NVDA\", \"2026-09-01\")\nprint(decision)\n```\n\nThe quick model serves the analysts, researchers, debaters and trader; the deep model serves the research and portfolio managers. Each can run on its own provider, for example the managers on Claude while the rest run on OpenAI:\n\n```python\nconfig[\"deep_think_provider\"] = \"anthropic\"\nconfig[\"deep_think_llm\"] = \"claude-opus-5-5\"\n```\n\nA tier on its own provider uses that provider's key and default endpoint; set `quick_think_backend_url` or `deep_think_backend_url` for a local or relay endpoint. The `TRADINGAGENTS_DEEP_THINK_PROVIDER` and `TRADINGAGENTS_QUICK_THINK_PROVIDER` variables set them for the CLI, together with the tier's model variable.\n\nSee `tradingagents/default_config.py` for all configuration options.\n\n### Fundamentals as filed\n\nUS company statements come from SEC EDGAR, which records the date every figure was filed. A run dated in the past reads the statements exactly as they stood that day: a fiscal year that has ended but has not been filed yet is not served, and a figure restated later still reads as first reported. Apple's 2008 total assets were filed as $39.6B and restated to $36.2B in 2010, so a run dated in between reads $39.6B. EDGAR needs no account or API key.\n\nOther companies' statements come from Yahoo Finance, which dates a statement by the period it covers rather than by when it was published. A run dated today reads them; a run dated in the past is told they are withheld, since Yahoo cannot say which figures were public by then.\n\nInsider trades are dated by when they happened, not when they were filed, so a run dated in the past is told they are withheld as well.\n\nSEC asks callers to identify themselves and refuses requests that carry no contact address, so a default one is sent. Set your own so SEC can reach you rather than the project:\n\n```bash\nSEC_EDGAR_USER_AGENT=\"Your Name your@email.com\"\n```\n\nIt covers companies that file with the SEC, including foreign companies listed in the US. Anything else, such as Hong Kong or A-share listings, falls through to the next vendor in the chain. EDGAR's machine-readable filings begin in 2009, and a fourth quarter is reported as unavailable rather than derived, because filers publish it only inside the annual figure.\n\n### Current holdings\n\nBy default the agents do not know what you hold, so their guidance is written for a reader who applies it to their own position. Pass a portfolio to have the trader, the risk analysts and the portfolio manager work against your actual book.\n\n```python\nfrom tradingagents.portfolio import PortfolioContext\n\nportfolio = PortfolioContext.model_validate({\n \"cash\": 25000.0,\n \"currency\": \"USD\",\n \"positions\": [{\"ticker\": \"NVDA\", \"quantity\": 120, \"average_price\": 150.0}],\n})\n_, decision = ta.propagate(\"NVDA\", \"2026-09-01\", portfolio=portfolio)\n```\n\nThe CLI takes the same content as a JSON file: `tradingagents --portfolio my_book.json`.\n\nAn empty `positions` list means a flat book, which is different from passing nothing. A run without a portfolio is never treated as flat.\n\n## Persistence and Recovery\n\nTradingAgents persists two kinds of state across runs.\n\n### Memory log\n\nThe memory log is always on. Each completed run appends its decision to `~/.tradingagents/memory/trading_memory.md`. While the analysts of a later run work, TradingAgents settles every logged decision whose holding period has passed: it fetches the realised return (raw, and alpha against the instrument's regional benchmark) and generates a one-paragraph reflection. The Portfolio Manager then reads the most recent decisions for the same ticker plus recent lessons from other tickers, so each analysis carries forward what worked and what didn't. If settling fails, the run goes on and its report says so.\n\nOverride the path with `TRADINGAGENTS_MEMORY_LOG_PATH`.\n\nTo settle decisions without running an analysis, for a scheduled job, call `ta.settle_all_pending()`; it returns the decisions it settled and any it could not.\n\n### Checkpoint resume\n\nCheckpoint resume is opt-in via `--checkpoint`. When enabled, LangGraph saves state after each node so a crashed or interrupted run resumes from the last successful step instead of starting over. The run view says whether it resumed a saved run or started fresh. Checkpoints are cleared automatically on successful completion.\n\nPer-ticker SQLite databases live at `~/.tradingagents/cache/checkpoints/.db` (override the base with `TRADINGAGENTS_CACHE_DIR`). Use `--clear-checkpoints` to reset all of them before a run.\n\n```bash\ntradingagents --checkpoint # enable for this run\ntradingagents --clear-checkpoints # reset before running\n```\n\n```python\nconfig = DEFAULT_CONFIG.copy()\nconfig[\"checkpoint_enabled\"] = True\nta = TradingAgentsGraph(config=config)\n_, decision = ta.propagate(\"NVDA\", \"2026-09-01\")\n```\n\n## Evaluating decisions over time\n\nOne run gives one decision, which cannot tell you whether the system decides well. `run_backtest` runs the same pipeline over a grid of tickers and dates, writes to a memory log of its own, and scores the decisions whose holding window has since traded.\n\n```python\nfrom tradingagents.backtest import iter_grid, run_backtest, summarize\n\ndates = iter_grid(\"2026-06-01\", \"2026-08-01\", every_n_days=7)\nresult = run_backtest([\"NVDA\", \"AAPL\"], dates, config, selected_analysts=[\"market\", \"news\"])\nprint(summarize(result).render())\n```\n\nFrom the CLI:\n\n```bash\ntradingagents backtest NVDA,AAPL --start 2026-06-01 --end 2026-08-01 --every 7\n```\n\nEach cell is scored on realized alpha against the instrument's regional benchmark, grouped by rating. A decision is entered at the first close after its date, because a run reads news published after that day's close. Your own memory log is never written to, and re-running the same grid with `run_id=result.run_id` skips the cells that already ran, so an interrupted sweep continues where it stopped.\n\n## Reproducibility\n\nTradingAgents is LLM-driven, so two runs of the same ticker and date can differ. This is expected for a research tool built on language models, not a defect. The variation comes from a few distinct sources, and it helps to separate them.\n\nLanguage model sampling is non-deterministic. Even at a fixed temperature, providers do not guarantee byte-identical output across calls, and reasoning models (the default GPT-6 family, and any thinking-mode model) vary the most because their internal reasoning is itself sampled.\n\nLive data moves. News, StockTwits, and Reddit return different content as time passes, so a run today sees different inputs than a run last week even for the same historical trade date. Pin the analysis date to hold the price and indicator window fixed, but the social and news sources still reflect \"now\".\n\nTo reduce variation you can lower the sampling temperature. Set `temperature` in your config (or `TRADINGAGENTS_TEMPERATURE` in `.env`); lower values make models that honor it more repeatable. The current curated models are reasoning-first and largely ignore temperature, so for tighter reproducibility name a non-reasoning model in your config, or in `TRADINGAGENTS_DEEP_THINK_LLM` and `TRADINGAGENTS_QUICK_THINK_LLM`. Any model ID your provider serves is accepted, whether or not the picker lists it.\n\n```python\nconfig = DEFAULT_CONFIG.copy()\nconfig[\"llm_provider\"] = \"openai\"\nconfig[\"temperature\"] = 0.0\n# Reasoning models ignore temperature. For tighter reproducibility, name a\n# non-reasoning model in deep_think_llm / quick_think_llm.\n```\n\nWhat does not vary anymore: the analyzed company identity is resolved deterministically from the ticker before any agent runs, and the market analyst grounds exact price and indicator claims in a verified data snapshot. Earlier reports of \"different companies\" or fabricated price levels across runs are addressed by these two mechanisms.\n\nBacktest results are not guaranteed to match any published figure. Returns depend on the model, the temperature, the date range, data quality, and the sampling above. Treat the framework as a research scaffold for studying multi-agent analysis, not as a strategy with a fixed, replicable return.\n\n## Contributing\n\nContributions are welcome: bug fixes, documentation, and feature ideas; past contributions are credited per release in [`CHANGELOG.md`](CHANGELOG.md).\n\n## Citation\n\nPlease reference our work if you find *TradingAgents* provides you with some help :)\n\n```\n@misc{xiao2025tradingagentsmultiagentsllmfinancial,\n title={TradingAgents: Multi-Agents LLM Financial Trading Framework}, \n author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},\n year={2025},\n eprint={2412.20138},\n archivePrefix={arXiv},\n primaryClass={q-fin.TR},\n url={https://arxiv.org/abs/2412.20138}, \n}\n```\n" }, { "repo": "virattt/ai-hedge-fund", "content": "# AI Hedge Fund\n\nThis is a proof of concept for an AI-powered hedge fund. The goal of this project is to explore the use of AI to make trading decisions. This project is for **educational** purposes only and is not intended for real trading or investment.\n\n\"image\"\n\n\nNote: the system does not actually make any trades.\n\n[![Twitter Follow](https://img.shields.io/twitter/follow/virattt?style=social)](https://twitter.com/virattt)\n\n## Disclaimer\n\nThis project is for **educational and research purposes only**.\n\n- Not intended for real trading or investment\n- No investment advice or guarantees provided\n- Creator assumes no liability for financial losses\n- Consult a financial advisor for investment decisions\n- Past performance does not indicate future results\n\nBy using this software, you agree to use it solely for learning purposes.\n\n## How to Install\n\n```bash\npipx install aihf\n```\n\n(or `uv tool install aihf`, or `pip install aihf` into an environment of your choice)\n\nThen run it from anywhere:\n\n```bash\naihf\n```\n\n### API keys\n\nThe app asks for keys the first time it needs them and saves them to `~/.hedge-fund/.env` — nothing to configure up front. It needs:\n\n- A [Financial Datasets](https://financialdatasets.ai) API key, for prices, fundamentals, and earnings. Or, instead of a key, the private key of a Base wallet holding USDC: every call then pays for itself with [x402](https://docs.financialdatasets.ai/x402) (`pip install 'aihf[x402]'`; last 365 days of data, non-professional use).\n- One model API key for the investor agents. Supported providers: Anthropic, OpenAI, DeepSeek, Google, xAI, Kimi, TypeSafe (Jev).\n\nKeys exported in your shell always win over the saved file.\n\n## How to Run\n\n### Interactive app\n\n```bash\naihf\n```\n\nWith no arguments, this launches the interactive terminal app. It has two modes:\n\n- **Paper trading** — your funds, each with a ledger of real market days and fake money. Highlight a fund to see what its next run will do; press enter to run it through the next completed session (an approval step shows the exact decision about to execute before anything trades), `s` for its full session history, `h`/`r` for the kill switch. Build a new fund from the same list: strategies, capital, cadence, tickers, and it is live. Every session is appended to a hash-chained ledger in `~/.hedge-fund/paper//`, so NAV is a track record, not a reset.\n- **Backtesting** — replay a fund over history and watch its equity curve draw against its benchmark. Results are saved to `~/.hedge-fund/research/`. Fund definitions live in `~/.hedge-fund/mandates/` and carry no tickers; you pick the universe per backtest.\n\n\n\n## Development\n\n```bash\ngit clone https://github.com/virattt/ai-hedge-fund.git\ncd ai-hedge-fund\npoetry install\npoetry run aihf\npoetry run pytest hedge_fund\n```\n\n## How to Contribute\n\n1. Fork the repository\n2. Create a feature branch\n3. Commit your changes\n4. Push to the branch\n5. Create a Pull Request\n\n**Important**: Please keep your pull requests small and focused. This will make it easier to review and merge.\n\n## Feature Requests\n\nIf you have a feature request, please open an [issue](https://github.com/virattt/ai-hedge-fund/issues) and make sure it is tagged with `enhancement`.\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n" }, { "repo": "OpenBB-finance/OpenBB", "content": null }, { "repo": "freqtrade/freqtrade", "content": "# ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)\n\n[![Freqtrade CI](https://github.com/freqtrade/freqtrade/actions/workflows/ci.yml/badge.svg?branch=develop)](https://github.com/freqtrade/freqtrade/actions/workflows/ci.yml)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)\n[![codecov](https://codecov.io/gh/freqtrade/freqtrade/branch/develop/graph/badge.svg?token=AD5BG3ATKI)](https://codecov.io/gh/freqtrade/freqtrade)\n[![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)\n[![Discord Server](https://img.shields.io/badge/Freqtrade_Discord-4E4E4E?logo=discord)](https://discord.gg/p7nuUNVfP7)\n\nFreqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.\n\n![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade-screenshot.png)\n\n## Disclaimer\n\nThis software is for educational purposes only. Do not risk money which\nyou are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS\nAND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.\n\nAlways start by running a trading bot in Dry-Run and do not engage money\nbefore you understand how it works and what profit/loss you should\nexpect.\n\nWe strongly recommend you to have coding and Python knowledge. Do not\nhesitate to read the source code and understand the mechanism of this bot.\n\n## Supported Exchange marketplaces\n\nPlease read the [exchange-specific notes](https://www.freqtrade.io/en/stable/exchanges/) to learn about special configurations that maybe needed for each exchange.\n\n### Supported Spot Exchanges\n\n- [X] [Binance](https://www.binance.com/)\n- [X] [BingX](https://bingx.com/invite/0EM9RX)\n- [X] [Bitget](https://www.bitget.com/)\n- [X] [Bybit EU](https://bybit.eu/)\n- [X] [Bybit](https://bybit.com/)\n- [X] [Gate EU](https://www.gate.com/en-eu)\n- [X] [Gate](https://www.gate.com/ref/6266643)\n- [X] [HTX](https://www.htx.com/)\n- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)\n- [X] [Kraken](https://kraken.com/)\n- [X] [MyOKX](https://okx.com/) (OKX EEA)\n- [X] [OKX](https://okx.com/)\n- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_\n\n### Supported Futures Exchanges\n\n- [X] [Binance](https://www.binance.com/)\n- [X] [Bitget](https://www.bitget.com/)\n- [X] [Bybit](https://bybit.com/)\n- [X] [Gate](https://www.gate.com/ref/6266643)\n- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)\n- [X] [Kraken](https://www.kraken.com/features/futures)\n- [X] [OKX](https://okx.com/)\n\nPlease make sure to read the [exchange specific notes](https://www.freqtrade.io/en/stable/exchanges/), as well as the [trading with leverage](https://www.freqtrade.io/en/stable/leverage/) documentation before diving in.\n\n### Community tested\n\nExchanges confirmed working by the community:\n\n- [X] [Bitvavo](https://bitvavo.com/)\n- [X] [Kucoin](https://www.kucoin.com/)\n\n## Documentation\n\nWe invite you to read the bot documentation to ensure you understand how the bot is working.\n\nPlease find the complete documentation on the [freqtrade website](https://www.freqtrade.io).\n\n## Features\n\n- [x] **Based on Python 3.12+**: For botting on any operating system - Windows, macOS and Linux.\n- [x] **Persistence**: Persistence is achieved through sqlite.\n- [x] **Dry-run**: Run the bot without paying money.\n- [x] **Backtesting**: Run a simulation of your buy/sell strategy.\n- [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.\n- [X] **Adaptive prediction modeling**: Build a smart strategy with FreqAI that self-trains to the market via adaptive machine learning methods. [Learn more](https://www.freqtrade.io/en/stable/freqai/)\n- [x] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade or use dynamic whitelists.\n- [x] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.\n- [x] **Builtin WebUI**: Builtin web UI to manage your bot.\n- [x] **Manageable via Telegram**: Manage the bot with Telegram.\n- [x] **Display profit/loss in fiat**: Display your profit/loss in fiat currency.\n- [x] **Performance status report**: Provide a performance status of your current trades.\n\n## Quick start\n\nPlease refer to the [Docker Quickstart documentation](https://www.freqtrade.io/en/stable/docker_quickstart/) on how to get started quickly.\n\nFor further (native) installation methods, please refer to the [Installation documentation page](https://www.freqtrade.io/en/stable/installation/).\n\n## Basic Usage\n\n### Bot commands\n\n```\nusage: freqtrade [-h] [-V]\n {trade,create-userdir,new-config,show-config,new-strategy,download-data,convert-data,convert-trade-data,trades-to-ohlcv,list-data,backtesting,backtesting-show,backtesting-analysis,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-markets,list-pairs,list-strategies,list-hyperoptloss,list-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}\n ...\n\nFree, open source crypto trading bot\n\npositional arguments:\n {trade,create-userdir,new-config,show-config,new-strategy,download-data,convert-data,convert-trade-data,trades-to-ohlcv,list-data,backtesting,backtesting-show,backtesting-analysis,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-markets,list-pairs,list-strategies,list-hyperoptloss,list-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}\n trade Trade module.\n create-userdir Create user-data directory.\n new-config Create new config\n show-config Show resolved config\n new-strategy Create new strategy\n download-data Download backtesting data.\n convert-data Convert candle (OHLCV) data from one format to\n another.\n convert-trade-data Convert trade data from one format to another.\n trades-to-ohlcv Convert trade data to OHLCV data.\n list-data List downloaded data.\n backtesting Backtesting module.\n backtesting-show Show past Backtest results\n backtesting-analysis\n Backtest Analysis module.\n hyperopt Hyperopt module.\n hyperopt-list List Hyperopt results\n hyperopt-show Show details of Hyperopt results\n list-exchanges Print available exchanges.\n list-markets Print markets on exchange.\n list-pairs Print pairs on exchange.\n list-strategies Print available strategies.\n list-hyperoptloss Print available hyperopt loss functions.\n list-freqaimodels Print available freqAI models.\n list-timeframes Print available timeframes for the exchange.\n show-trades Show trades.\n test-pairlist Test your pairlist configuration.\n convert-db Migrate database to different system\n install-ui Install FreqUI\n plot-dataframe Plot candles with indicators.\n plot-profit Generate plot showing profits.\n webserver Webserver module.\n strategy-updater updates outdated strategy files to the current version\n lookahead-analysis Check for potential look ahead bias.\n recursive-analysis Check for potential recursive formula issue.\n\noptions:\n -h, --help show this help message and exit\n -V, --version show program's version number and exit\n```\n\n### Telegram RPC commands\n\nTelegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/stable/telegram-usage/)\n\n- `/start`: Starts the trader.\n- `/stop`: Stops the trader.\n- `/stopentry`: Stop entering new trades.\n- `/status |[table]`: Lists all or specific open trades.\n- `/profit []`: Lists cumulative profit from all finished trades, over the last n days.\n- `/profit_long []`: Lists cumulative profit from all finished long trades, over the last n days.\n- `/profit_short []`: Lists cumulative profit from all finished short trades, over the last n days.\n- `/forceexit |all`: Instantly exits the given trade (Ignoring `minimum_roi`).\n- `/fx |all`: Alias to `/forceexit`\n- `/performance`: Show performance of each finished trade grouped by pair\n- `/balance`: Show account balance per currency.\n- `/daily `: Shows profit or loss per day, over the last n days.\n- `/help`: Show help message.\n- `/version`: Show version.\n\n\n## Development branches\n\nThe project is currently setup in two main branches:\n\n- `develop` - This branch has often new features, but might also contain breaking changes. We try hard to keep this branch as stable as possible.\n- `stable` - This branch contains the latest stable release. This branch is generally well tested.\n- `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.\n\n## Support\n\n### Help / Discord\n\nFor any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join the Freqtrade [discord server](https://discord.gg/p7nuUNVfP7).\n\n### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)\n\nIf you discover a bug in the bot, please\n[search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)\nfirst. If it hasn't been reported, please\n[create a new issue](https://github.com/freqtrade/freqtrade/issues/new/choose) and\nensure you follow the template guide so that the team can assist you as\nquickly as possible.\n\nFor every [issue](https://github.com/freqtrade/freqtrade/issues/new/choose) created, kindly follow up and mark satisfaction or reminder to close issue when equilibrium ground is reached.\n\n--Maintain github's [community policy](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct)--\n\n### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)\n\nHave you a great idea to improve the bot you want to share? Please,\nfirst search if this feature was not [already discussed](https://github.com/freqtrade/freqtrade/labels/enhancement).\nIf it hasn't been requested, please\n[create a new request](https://github.com/freqtrade/freqtrade/issues/new/choose)\nand ensure you follow the template guide so that it does not get lost\nin the bug reports.\n\n### [Pull Requests](https://github.com/freqtrade/freqtrade/pulls)\n\nFeel like the bot is missing a feature? We welcome your pull requests!\n\nPlease read the\n[Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)\nto understand the requirements before sending your pull-requests.\n\nCoding is not a necessity to contribute - maybe start with improving the documentation?\nIssues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.\n\n**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/p7nuUNVfP7) (please use the #dev channel for this). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.\n\n**Important:** Always create your PR against the `develop` branch, not `stable`.\n\n## Requirements\n\n### Up-to-date clock\n\nThe clock must be accurate, synchronized to a NTP server very frequently to avoid problems with communication to the exchanges.\n\n### Minimum hardware required\n\nTo run this bot we recommend you a cloud instance with a minimum of:\n\n- Minimal (advised) system requirements: 2GB RAM, 1GB disk space, 2vCPU\n\n### Software requirements\n\n- [Python >= 3.12](http://docs.python-guide.org/en/latest/starting/installation/)\n- [pip](https://pip.pypa.io/en/stable/installing/)\n- [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)\n- [TA-Lib](https://ta-lib.github.io/ta-lib-python/)\n- [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)\n- [Docker](https://www.docker.com/products/docker) (Recommended)\n" }, { "repo": "microsoft/qlib", "content": "[![Python Versions](https://img.shields.io/pypi/pyversions/pyqlib.svg?logo=python&logoColor=white)](https://pypi.org/project/pyqlib/#files)\n[![Platform](https://img.shields.io/badge/platform-linux%20%7C%20windows%20%7C%20macos-lightgrey)](https://pypi.org/project/pyqlib/#files)\n[![PypI Versions](https://img.shields.io/pypi/v/pyqlib)](https://pypi.org/project/pyqlib/#history)\n[![Upload Python Package](https://github.com/microsoft/qlib/workflows/Upload%20Python%20Package/badge.svg)](https://pypi.org/project/pyqlib/)\n[![Github Actions Test Status](https://github.com/microsoft/qlib/workflows/Test/badge.svg?branch=main)](https://github.com/microsoft/qlib/actions)\n[![Documentation Status](https://readthedocs.org/projects/qlib/badge/?version=latest)](https://qlib.readthedocs.io/en/latest/?badge=latest)\n[![License](https://img.shields.io/pypi/l/pyqlib)](LICENSE)\n[![Join the chat at https://gitter.im/Microsoft/qlib](https://badges.gitter.im/Microsoft/qlib.svg)](https://gitter.im/Microsoft/qlib?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)\n\n## Unreleased breaking change: artifact loading\n\nNew source builds introduce a **backward-incompatible safety default**: recorder\nloads are restricted. If you reload saved models/datasets, resume online or delayed\ntraining, or use DDG-DA, verify the artifact writer and storage permissions before\nopting in with `trusted=True` (CLI: `--trusted=True`). Fresh in-memory training and\nsupported data-only reads need no opt-in. HIST mappings and high-frequency cache\npaths also have migration requirements.\n\nFollow the [artifact loading migration guide](https://qlib.readthedocs.io/en/latest/start/artifact_migration.html)\nbefore upgrading. Merging into `main` affects source installs before a new PyPI\nrelease; this change remains **unreleased until the first tagged release containing\nit**. That release's versioned upgrade notes should link to the same guide.\n\n> **Upgrading configuration-driven workflows?** See the\n> [configuration migration guide](docs/start/config_migration.rst) for the\n> unreleased source changes: per-component `trusted: true` for local `.py`\n> imports, restricted expressions, and explicit extension mappings. Package\n> imports remain available. Match this guide to your checkout; PR source,\n> `main`, and tagged/PyPI releases may differ.\n\n## :newspaper: **What's NEW!**   :sparkling_heart: \n\nRecent released features\n\n### Introducing \"RD_Agent\": LLM-Based Autonomous Evolving Agents for Industrial Data-Driven R&D\n\nWe are excited to announce the release of **RD-Agent**📢, a powerful tool that supports automated factor mining and model optimization in quant investment R&D.\n\nRD-Agent is now available on [GitHub](https://github.com/microsoft/RD-Agent), and we welcome your star🌟!\n\nTo learn more, please visit the [RD-Agent repository](https://github.com/microsoft/RD-Agent). We have prepared several public demo videos for you:\n\n| Scenario | Demo video (English) | Demo video (中文) |\n| -- | ------ | ------ |\n| Quant Factor Mining | [YouTube](https://www.youtube.com/watch?v=X4DK2QZKaKY&t=6s) | [YouTube](https://www.youtube.com/watch?v=X4DK2QZKaKY&t=6s) |\n| Quant Factor Mining from reports | [YouTube](https://www.youtube.com/watch?v=ECLTXVcSx-c) | [YouTube](https://www.youtube.com/watch?v=ECLTXVcSx-c) |\n| Quant Model Optimization | [YouTube](https://www.youtube.com/watch?v=dm0dWL49Bc0&t=104s) | [YouTube](https://www.youtube.com/watch?v=dm0dWL49Bc0&t=104s) |\n\n- 📃**Paper**: [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://arxiv.org/abs/2505.15155)\n- 👾**Code**: https://github.com/microsoft/RD-Agent/\n```BibTeX\n@misc{li2025rdagentquant,\n title={R\\&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization},\n author={Yuante Li and Xu Yang and Xiao Yang and Minrui Xu and Xisen Wang and Weiqing Liu and Jiang Bian},\n year={2025},\n eprint={2505.15155},\n archivePrefix={arXiv},\n primaryClass={cs.AI}\n}\n```\n![image](https://github.com/user-attachments/assets/3198bc10-47ba-4ee0-8a8e-46d5ce44f45d)\n\n***\n\n| Feature | Status |\n| -- | ------ |\n| [R&D-Agent-Quant](https://arxiv.org/abs/2505.15155) Published | Apply R&D-Agent to Qlib for quant trading | \n| BPQP for End-to-end learning | 📈Coming soon!([Under review](https://github.com/microsoft/qlib/pull/1863)) |\n| 🔥LLM-driven Auto Quant Factory🔥 | 🚀 Released in [♾️RD-Agent](https://github.com/microsoft/RD-Agent) on Aug 8, 2024 |\n| KRNN and Sandwich models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1414/) on May 26, 2023 |\n| Release Qlib v0.9.0 | :octocat: [Released](https://github.com/microsoft/qlib/releases/tag/v0.9.0) on Dec 9, 2022 |\n| RL Learning Framework | :hammer: :chart_with_upwards_trend: Released on Nov 10, 2022. [#1332](https://github.com/microsoft/qlib/pull/1332), [#1322](https://github.com/microsoft/qlib/pull/1322), [#1316](https://github.com/microsoft/qlib/pull/1316),[#1299](https://github.com/microsoft/qlib/pull/1299),[#1263](https://github.com/microsoft/qlib/pull/1263), [#1244](https://github.com/microsoft/qlib/pull/1244), [#1169](https://github.com/microsoft/qlib/pull/1169), [#1125](https://github.com/microsoft/qlib/pull/1125), [#1076](https://github.com/microsoft/qlib/pull/1076)|\n| HIST and IGMTF models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1040) on Apr 10, 2022 |\n| Qlib [notebook tutorial](https://github.com/microsoft/qlib/tree/main/examples/tutorial) | 📖 [Released](https://github.com/microsoft/qlib/pull/1037) on Apr 7, 2022 | \n| Ibovespa index data | :rice: [Released](https://github.com/microsoft/qlib/pull/990) on Apr 6, 2022 |\n| Point-in-Time database | :hammer: [Released](https://github.com/microsoft/qlib/pull/343) on Mar 10, 2022 |\n| Arctic Provider Backend & Orderbook data example | :hammer: [Released](https://github.com/microsoft/qlib/pull/744) on Jan 17, 2022 |\n| Meta-Learning-based framework & DDG-DA | :chart_with_upwards_trend: :hammer: [Released](https://github.com/microsoft/qlib/pull/743) on Jan 10, 2022 | \n| Planning-based portfolio optimization | :hammer: [Released](https://github.com/microsoft/qlib/pull/754) on Dec 28, 2021 | \n| Release Qlib v0.8.0 | :octocat: [Released](https://github.com/microsoft/qlib/releases/tag/v0.8.0) on Dec 8, 2021 |\n| ADD model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/704) on Nov 22, 2021 |\n| ADARNN model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/689) on Nov 14, 2021 |\n| TCN model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/668) on Nov 4, 2021 |\n| Nested Decision Framework | :hammer: [Released](https://github.com/microsoft/qlib/pull/438) on Oct 1, 2021. [Example](https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution/workflow.py) and [Doc](https://qlib.readthedocs.io/en/latest/component/highfreq.html) |\n| Temporal Routing Adaptor (TRA) | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/531) on July 30, 2021 |\n| Transformer & Localformer | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/508) on July 22, 2021 |\n| Release Qlib v0.7.0 | :octocat: [Released](https://github.com/microsoft/qlib/releases/tag/v0.7.0) on July 12, 2021 |\n| TCTS Model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/491) on July 1, 2021 |\n| Online serving and automatic model rolling | :hammer: [Released](https://github.com/microsoft/qlib/pull/290) on May 17, 2021 | \n| DoubleEnsemble Model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/286) on Mar 2, 2021 | \n| High-frequency data processing example | :hammer: [Released](https://github.com/microsoft/qlib/pull/257) on Feb 5, 2021 |\n| High-frequency trading example | :chart_with_upwards_trend: [Part of code released](https://github.com/microsoft/qlib/pull/227) on Jan 28, 2021 | \n| High-frequency data(1min) | :rice: [Released](https://github.com/microsoft/qlib/pull/221) on Jan 27, 2021 |\n| Tabnet Model | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/205) on Jan 22, 2021 |\n\nFeatures released before 2021 are not listed here.\n\n

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\n\nQlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning.\n\nAn increasing number of SOTA Quant research works/papers in diverse paradigms are being released in Qlib to collaboratively solve key challenges in quantitative investment. For example, 1) using supervised learning to mine the market's complex non-linear patterns from rich and heterogeneous financial data, 2) modeling the dynamic nature of the financial market using adaptive concept drift technology, and 3) using reinforcement learning to model continuous investment decisions and assist investors in optimizing their trading strategies.\n\nIt contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. \nFor more details, please refer to our paper [\"Qlib: An AI-oriented Quantitative Investment Platform\"](https://arxiv.org/abs/2009.11189).\n\n\n\n \n \n \n \n \n \n \n \n \n \n
Frameworks, Tutorial, Data & DevOpsMain Challenges & Solutions in Quant Research
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  • Plans
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  • Framework of Qlib
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  • Quick Start
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  • Quant Dataset Zoo
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  • Learning Framework
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  • More About Qlib
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  • Offline Mode and Online Mode\n \n
  • Related Reports
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  • Contact Us
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  • Contributing
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  • Main Challenges & Solutions in Quant Research\n \n
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    \n\n# Plans\nNew features under development(order by estimated release time).\nYour feedbacks about the features are very important.\n\n\n\n# Framework of Qlib\n\n
    \n\n
    \n\nThe high-level framework of Qlib can be found above(users can find the [detailed framework](https://qlib.readthedocs.io/en/latest/introduction/introduction.html#framework) of Qlib's design when getting into nitty gritty).\nThe components are designed as loose-coupled modules, and each component could be used stand-alone.\n\nQlib provides a strong infrastructure to support Quant research. [Data](https://qlib.readthedocs.io/en/latest/component/data.html) is always an important part.\nA strong learning framework is designed to support diverse learning paradigms (e.g. [reinforcement learning](https://qlib.readthedocs.io/en/latest/component/rl.html), [supervised learning](https://qlib.readthedocs.io/en/latest/component/workflow.html#model-section)) and patterns at different levels(e.g. [market dynamic modeling](https://qlib.readthedocs.io/en/latest/component/meta.html)).\nBy modeling the market, [trading strategies](https://qlib.readthedocs.io/en/latest/component/strategy.html) will generate trade decisions that will be executed. Multiple trading strategies and executors in different levels or granularities can be [nested to be optimized and run together](https://qlib.readthedocs.io/en/latest/component/highfreq.html).\nAt last, a comprehensive [analysis](https://qlib.readthedocs.io/en/latest/component/report.html) will be provided and the model can be [served online](https://qlib.readthedocs.io/en/latest/component/online.html) in a low cost.\n\n\n# Quick Start\n\nThis quick start guide tries to demonstrate\n1. It's very easy to build a complete Quant research workflow and try your ideas with _Qlib_.\n2. Though with *public data* and *simple models*, machine learning technologies **work very well** in practical Quant investment.\n\nHere is a quick **[demo](https://terminalizer.com/view/3f24561a4470)** shows how to install ``Qlib``, and run LightGBM with ``qrun``. **But**, please make sure you have already prepared the data following the [instruction](#data-preparation).\n\n\n## Installation\n\nThis table demonstrates the supported Python version of `Qlib`:\n| | install with pip | install from source | plot |\n| ------------- |:---------------------:|:--------------------:|:------------------:|\n| Python 3.8 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| Python 3.9 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| Python 3.10 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| Python 3.11 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n| Python 3.12 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |\n\n**Note**: \n1. **Conda** is suggested for managing your Python environment. In some cases, using Python outside of a `conda` environment may result in missing header files, causing the installation failure of certain packages.\n2. Please pay attention that installing cython in Python 3.6 will raise some error when installing ``Qlib`` from source. If users use Python 3.6 on their machines, it is recommended to *upgrade* Python to version 3.8 or higher, or use `conda`'s Python to install ``Qlib`` from source.\n\n### Install with pip\nUsers can easily install ``Qlib`` by pip according to the following command.\n\n```bash\n pip install pyqlib\n```\n\n**Note**: pip will install the latest stable qlib. However, the main branch of qlib is in active development. If you want to test the latest scripts or functions in the main branch. Please install qlib with the methods below.\n\n### Install from source\nAlso, users can install the latest dev version ``Qlib`` by the source code according to the following steps:\n\n* Before installing ``Qlib`` from source, users need to install some dependencies:\n\n ```bash\n pip install numpy\n pip install --upgrade cython\n ```\n\n* Clone the repository and install ``Qlib`` as follows.\n ```bash\n git clone https://github.com/microsoft/qlib.git && cd qlib\n pip install . # `pip install -e .[dev]` is recommended for development. check details in docs/developer/code_standard_and_dev_guide.rst\n ```\n\n**Tips**: If you fail to install `Qlib` or run the examples in your environment, comparing your steps and the [CI workflow](.github/workflows/test_qlib_from_source.yml) may help you find the problem.\n\n**Tips for Mac**: If you are using Mac with M1, you might encounter issues in building the wheel for LightGBM, which is due to missing dependencies from OpenMP. To solve the problem, install openmp first with ``brew install libomp`` and then run ``pip install .`` to build it successfully. \n\n## Data Preparation\n❗ Due to more restrict data security policy. The official dataset is disabled temporarily. You can try [this data source](https://github.com/chenditc/investment_data/releases) contributed by the community.\nHere is an example to download the latest data.\n```bash\nwget https://github.com/chenditc/investment_data/releases/latest/download/qlib_bin.tar.gz\nmkdir -p ~/.qlib/qlib_data/cn_data\ntar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1\nrm -f qlib_bin.tar.gz\n```\n\nThe official dataset below will resume in short future.\n\n\n----\n\nLoad and prepare data by running the following code:\n\n### Get with module\n ```bash\n # get 1d data\n python -m qlib.cli.data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn\n\n # get 1min data\n python -m qlib.cli.data qlib_data --target_dir ~/.qlib/qlib_data/cn_data_1min --region cn --interval 1min\n\n ```\n\n### Get from source\n\n ```bash\n # get 1d data\n python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn\n\n # get 1min data\n python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data_1min --region cn --interval 1min\n\n ```\n\nThis dataset is created by public data collected by [crawler scripts](scripts/data_collector/), which have been released in\nthe same repository.\nUsers could create the same dataset with it. [Description of dataset](https://github.com/microsoft/qlib/tree/main/scripts/data_collector#description-of-dataset)\n\n*Please pay **ATTENTION** that the data is collected from [Yahoo Finance](https://finance.yahoo.com/lookup), and the data might not be perfect.\nWe recommend users to prepare their own data if they have a high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)*.\n\n### Automatic update of daily frequency data (from yahoo finance)\n > This step is *Optional* if users only want to try their models and strategies on history data.\n > \n > It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically.\n >\n > **NOTE**: Users can't incrementally update data based on the offline data provided by Qlib(some fields are removed to reduce the data size). Users should use [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance) to download Yahoo data from scratch and then incrementally update it.\n > \n > For more information, please refer to: [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance)\n\n * Automatic update of data to the \"qlib\" directory each trading day(Linux)\n * use *crontab*: `crontab -e`\n * set up timed tasks:\n\n ```\n * * * * 1-5 python