[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-80793":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":8,"htmlUrl":8,"language":9,"languages":8,"totalLinesOfCode":8,"stars":10,"forks":11,"watchers":12,"openIssues":13,"contributorsCount":13,"subscribersCount":13,"size":13,"stars1d":13,"stars7d":13,"stars30d":13,"stars90d":13,"forks30d":13,"starsTrendScore":13,"compositeScore":14,"rankGlobal":8,"rankLanguage":8,"license":15,"archived":16,"fork":16,"defaultBranch":17,"hasWiki":18,"hasPages":16,"topics":19,"createdAt":8,"pushedAt":8,"updatedAt":20,"readmeContent":21,"aiSummary":22,"trendingCount":13,"starSnapshotCount":13,"syncStatus":23,"lastSyncTime":24,"discoverSource":25},80793,"intelligent-strategy-trading","Slickorps\u002Fintelligent-strategy-trading","Slickorps",null,"Python",38,81,1,0,42.74,"MIT License",false,"main",true,[],"2026-06-12 04:01:30","# Intelligent Strategy Trading\n\n![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.11+-blue.svg)\n![FastAPI](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFastAPI-0.115+-009688.svg)\n![Go](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fgo-1.21+-00ADD8.svg)\n![TypeScript](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTypeScript-5.x-3178C6.svg)\n![HTML](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FHTML-5-E34F26.svg)\n![CSS](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCSS-3-1572B6.svg)\n![Shell](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FShell-Bash-4EAA25.svg)\n![Docker](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDocker-ready-2496ED.svg)\n![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-MIT-yellow.svg)\n![Status](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fstatus-beta-orange.svg)\n\n> **Professional Multi-Asset Trading Platform with Visual Strategy Orchestration**\n\nA quantitative trading infrastructure engineered for portfolio management and cyclical asset allocation. Unlike high-frequency systems, this platform emphasizes **multi-factor modeling**, **dynamic risk budgeting**, and **path simulation** to achieve sustainable returns within controlled risk parameters.\n\n## Key Features\n\n- **Visual Strategy Logic**: Node-based strategy builder inspired by dataflow programming paradigms\n- **Multi-Factor Modeling**: Comprehensive factor analysis including momentum, volatility, and cross-asset correlation\n- **Dynamic Risk Budget**: Automatic rebalancing with configurable deviation thresholds\n- **Monte Carlo Simulation**: 10,000+ path simulations for drawdown probability analysis\n- **Stress Testing**: Historical scenario replay (2008 crisis, COVID crash)\n- **Multi-Asset Support**: Forex majors, index CFDs, commodities, crypto bluechips\n- **AI\u002FQuant Integration**: Native LangChain tools for LLM-powered analysis\n\n## Quick Start\n\nInstall the package:\n\n```bash\npip install -e \".[all]\"\n```\n\nStart the API server:\n\n```bash\nuvicorn ist.api.main:app --reload\n```\n\nExecute your first backtest in 10 lines:\n\n```python\nfrom ist.api.client import StrategyClient\n\nclient = StrategyClient(\"http:\u002F\u002Flocalhost:8000\")\n\n# Load conservative wealth preservation profile\nprofile = client.load_profile(\"conservative\")\n\n# Run backtest on 4 years of data\nresult = client.backtest.run(\n    strategy_config=profile,\n    start_date=\"2020-01-01\",\n    end_date=\"2023-12-31\"\n)\n\nprint(f\"Total Return: {result.total_return:.2%}\")\nprint(f\"Max Drawdown: {result.max_drawdown:.2%}\")\nprint(f\"Sharpe Ratio: {result.sharpe_ratio:.2f}\")\n```\n\n## Supported Markets & Data Schema\n\nAll market data follows a standardized OHLCV format:\n\n| Asset Class | Symbol Format | Example | Description |\n|-------------|---------------|---------|-------------|\n| **Forex Majors** | `CCYCCY` | `EURUSD` | Major currency pairs |\n| **Index CFDs** | `XXX000` | `SPX500` | Stock index contracts |\n| **Commodities** | `XXXUSD` | `XAUUSD` | Precious metals, energy |\n| **Crypto** | `CCCUSD` | `BTCUSD` | Bluechip cryptocurrencies |\n\n### Standard API Response Format\n\n```json\n{\n  \"timestamp\": \"2024-01-15T09:30:00Z\",\n  \"symbol\": \"EURUSD\",\n  \"open\": 1.0850,\n  \"high\": 1.0865,\n  \"low\": 1.0845,\n  \"close\": 1.0860,\n  \"volume\": 15420\n}\n```\n\n## Configuration Templates\n\nDefine your personalized asset allocation via JSON configuration:\n\n```json\n{\n  \"profile_name\": \"Long-term Wealth Growth\",\n  \"target_annual_return\": \"8% - 12%\",\n  \"max_drawdown_limit\": \"5%\",\n  \"asset_allocation\": {\n    \"forex_majors\": 0.40,\n    \"gold_commodities\": 0.20,\n    \"index_cfds\": 0.30,\n    \"crypto_bluechips\": 0.10\n  },\n  \"risk_management\": {\n    \"dynamic_rebalancing\": true,\n    \"rebalance_threshold\": \"3%\",\n    \"path_simulation_runs\": 10000,\n    \"stress_test_scenario\": \"2008_financial_crisis_replayed\"\n  },\n  \"strategy_nodes\": {\n    \"version\": \"1.0\",\n    \"nodes\": [\n      {\n        \"id\": \"data_source\",\n        \"type\": \"DataSourceNode\",\n        \"params\": {\"symbol\": \"EURUSD\", \"timeframe\": \"1h\"}\n      },\n      {\n        \"id\": \"sma_50\",\n        \"type\": \"IndicatorNode\",\n        \"params\": {\"indicator\": \"SMA\", \"period\": 50}\n      },\n      {\n        \"id\": \"sma_200\",\n        \"type\": \"IndicatorNode\",\n        \"params\": {\"indicator\": \"SMA\", \"period\": 200}\n      },\n      {\n        \"id\": \"golden_cross\",\n        \"type\": \"ConditionNode\",\n        \"params\": {\"condition\": \"cross_above\"}\n      },\n      {\n        \"id\": \"long_entry\",\n        \"type\": \"ActionNode\",\n        \"params\": {\"action\": \"buy\", \"size_pct\": 0.05}\n      }\n    ],\n    \"connections\": [\n      {\"from\": \"data_source\", \"to\": \"sma_50\"},\n      {\"from\": \"data_source\", \"to\": \"sma_200\"},\n      {\"from\": \"sma_50\", \"to\": \"golden_cross\"},\n      {\"from\": \"sma_200\", \"to\": \"golden_cross\"},\n      {\"from\": \"golden_cross\", \"to\": \"long_entry\"}\n    ]\n  }\n}\n```\n\n**Interpretation**: Allocates 40% to forex majors with dynamic rebalancing enabled. System validates through 10,000 path simulations to ensure max drawdown remains within 5% limit.\n\n## AI & Quantitative Integration\n\nThe platform exposes LangChain-compatible tools for LLM integration:\n\n```python\nfrom langchain.agents import AgentExecutor, create_openai_tools_agent\nfrom ist.integration.langchain_tools import (\n    GetMarketDataTool,\n    RunBacktestTool,\n    GetPortfolioTool,\n    AnalyzeRiskTool\n)\n\ntools = [\n    GetMarketDataTool(),\n    RunBacktestTool(),\n    GetPortfolioTool(),\n    AnalyzeRiskTool()\n]\n\n# Create agent for autonomous analysis\nagent = create_openai_tools_agent(llm, tools, prompt)\nexecutor = AgentExecutor(agent=agent, tools=tools)\n\nresponse = executor.invoke({\n    \"input\": \"Analyze current EURUSD trend and backtest a momentum strategy\"\n})\n```\n\n### Vector Database Integration\n\nStore and query strategy performance, market regimes, and research notes:\n\n```python\nfrom ist.integration.vector_store import StrategyMemory\n\nmemory = StrategyMemory()\nmemory.store_strategy_result(\n    strategy_id=\"trend_following_v1\",\n    result=backtest_result,\n    metadata={\"market_regime\": \"trending\", \"sharpe\": 1.85}\n)\n\n# Query similar successful strategies\nsimilar = memory.query_similar_strategies(\n    target_sharpe=1.80,\n    n_results=5\n)\n```\n\n## Architecture\n\n```\n┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐\n│   API Layer     │────▶│  Strategy Engine│────▶│  Visualization  │\n│   (FastAPI)     │     │  (Node Graph)   │     │  (Flowchart)    │\n└─────────────────┘     └─────────────────┘     └─────────────────┘\n         │                       │\n         ▼                       ▼\n┌─────────────────┐     ┌─────────────────┐\n│   Risk Engine   │◀───▶│  Backtest Engine│\n│ (Monte Carlo)   │     │ (Event-driven)  │\n└─────────────────┘     └─────────────────┘\n         │                       │\n         ▼                       ▼\n┌─────────────────┐     ┌─────────────────┐\n│   Data Layer    │     │  Execution Layer│\n│ (Provider API)  │     │ (Paper\u002FLive)    │\n└─────────────────┘     └─────────────────┘\n```\n\n## API Endpoints\n\n| Endpoint | Method | Description |\n|----------|--------|-------------|\n| `\u002Fhealth` | GET | Service health check |\n| `\u002Fstrategies` | GET\u002FPOST | List\u002Fcreate strategies |\n| `\u002Fstrategies\u002F{id}\u002Fflowchart` | GET | Get strategy visualization data |\n| `\u002Fstrategies\u002F{id}\u002Fbacktest` | POST | Run backtest |\n| `\u002Fbacktest\u002F{id}\u002Fresults` | GET | Retrieve backtest results |\n| `\u002Fportfolio\u002Fanalyze` | POST | Portfolio risk analysis |\n| `\u002Frisk\u002Fsimulate` | POST | Monte Carlo simulation |\n\nFull API documentation available at `\u002Fdocs` (Swagger UI) when server is running.\n\n## Development\n\n```bash\n# Clone repository\ngit clone https:\u002F\u002Fgithub.com\u002Fyourusername\u002Fintelligent-strategy-trading.git\ncd intelligent-strategy-trading\n\n# Install with development dependencies\npip install -e \".[dev]\"\n\n# Run tests\npytest tests\u002F -v --cov=ist\n\n# Type checking\nmypy src\u002F\n\n# Linting\nruff check src\u002F\nruff format src\u002F\n```\n\n## Roadmap\n\n- [x] Core API foundation\n- [x] Visual strategy nodes\n- [x] Event-driven backtest engine\n- [x] Multi-factor risk modeling\n- [x] Monte Carlo path simulation\n- [ ] Live broker integrations (Interactive Brokers, OANDA)\n- [ ] WebSocket real-time data\n- [ ] Web-based visual editor\n- [ ] Machine learning factor integration\n\n## Docker Deployment\n\n```bash\n# Quick start\ndocker-compose up -d\n\n# With PostgreSQL and Redis\ndocker-compose --profile production up -d\n\n# Access API\ncurl http:\u002F\u002Flocalhost:8000\u002Fhealth\n```\n\nSee [docs\u002Fdeployment.md](docs\u002Fdeployment.md) for detailed deployment options.\n\n## License\n\nMIT License - see [LICENSE](LICENSE) file.\n\n---\n\n**Disclaimer**: This software is for educational and research purposes. Trading financial instruments carries significant risk. Past performance does not guarantee future results.\n","Intelligent Strategy Trading 是一个专业的多资产交易平台，支持视觉化的策略编排。项目采用Python开发，并结合了FastAPI、Go、TypeScript等技术栈，具备节点式策略构建器、多因子模型分析、动态风险预算和蒙特卡洛模拟等功能，特别适用于需要长期稳定回报且控制风险的投资组合管理场景。此外，平台还支持多种资产类别（如外汇、指数差价合约、商品和加密货币）以及历史情景回放的压力测试，同时集成了AI\u002FQuant工具以增强量化分析能力。通过简洁的API接口与标准的数据格式，用户可以轻松地进行策略配置与回测执行。",2,"2026-06-11 04:02:20","CREATED_QUERY"]