Professional Multi-Asset Trading Platform with Visual Strategy Orchestration

A 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.

• Visual Strategy Logic: Node-based strategy builder inspired by dataflow programming paradigms
• Multi-Factor Modeling: Comprehensive factor analysis including momentum, volatility, and cross-asset correlation
• Dynamic Risk Budget: Automatic rebalancing with configurable deviation thresholds
• Monte Carlo Simulation: 10,000+ path simulations for drawdown probability analysis
• Stress Testing: Historical scenario replay (2008 crisis, COVID crash)
• Multi-Asset Support: Forex majors, index CFDs, commodities, crypto bluechips
• AI/Quant Integration: Native LangChain tools for LLM-powered analysis

Install the package:

pip install -e ".[all]"

Start the API server:

uvicorn ist.api.main:app --reload

Execute your first backtest in 10 lines:

from ist.api.client import StrategyClient

client = StrategyClient("http://localhost:8000")

# Load conservative wealth preservation profile
profile = client.load_profile("conservative")

# Run backtest on 4 years of data
result = client.backtest.run(
    strategy_config=profile,
    start_date="2020-01-01",
    end_date="2023-12-31"
)

print(f"Total Return: {result.total_return:.2%}")
print(f"Max Drawdown: {result.max_drawdown:.2%}")
print(f"Sharpe Ratio: {result.sharpe_ratio:.2f}")

All market data follows a standardized OHLCV format:

{
  "timestamp": "2024-01-15T09:30:00Z",
  "symbol": "EURUSD",
  "open": 1.0850,
  "high": 1.0865,
  "low": 1.0845,
  "close": 1.0860,
  "volume": 15420
}

Define your personalized asset allocation via JSON configuration:

{
  "profile_name": "Long-term Wealth Growth",
  "target_annual_return": "8% - 12%",
  "max_drawdown_limit": "5%",
  "asset_allocation": {
    "forex_majors": 0.40,
    "gold_commodities": 0.20,
    "index_cfds": 0.30,
    "crypto_bluechips": 0.10
  },
  "risk_management": {
    "dynamic_rebalancing": true,
    "rebalance_threshold": "3%",
    "path_simulation_runs": 10000,
    "stress_test_scenario": "2008_financial_crisis_replayed"
  },
  "strategy_nodes": {
    "version": "1.0",
    "nodes": [
      {
        "id": "data_source",
        "type": "DataSourceNode",
        "params": {"symbol": "EURUSD", "timeframe": "1h"}
      },
      {
        "id": "sma_50",
        "type": "IndicatorNode",
        "params": {"indicator": "SMA", "period": 50}
      },
      {
        "id": "sma_200",
        "type": "IndicatorNode",
        "params": {"indicator": "SMA", "period": 200}
      },
      {
        "id": "golden_cross",
        "type": "ConditionNode",
        "params": {"condition": "cross_above"}
      },
      {
        "id": "long_entry",
        "type": "ActionNode",
        "params": {"action": "buy", "size_pct": 0.05}
      }
    ],
    "connections": [
      {"from": "data_source", "to": "sma_50"},
      {"from": "data_source", "to": "sma_200"},
      {"from": "sma_50", "to": "golden_cross"},
      {"from": "sma_200", "to": "golden_cross"},
      {"from": "golden_cross", "to": "long_entry"}
    ]
  }
}

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.

The platform exposes LangChain-compatible tools for LLM integration:

from langchain.agents import AgentExecutor, create_openai_tools_agent
from ist.integration.langchain_tools import (
    GetMarketDataTool,
    RunBacktestTool,
    GetPortfolioTool,
    AnalyzeRiskTool
)

tools = [
    GetMarketDataTool(),
    RunBacktestTool(),
    GetPortfolioTool(),
    AnalyzeRiskTool()
]

# Create agent for autonomous analysis
agent = create_openai_tools_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

response = executor.invoke({
    "input": "Analyze current EURUSD trend and backtest a momentum strategy"
})

Store and query strategy performance, market regimes, and research notes:

from ist.integration.vector_store import StrategyMemory

memory = StrategyMemory()
memory.store_strategy_result(
    strategy_id="trend_following_v1",
    result=backtest_result,
    metadata={"market_regime": "trending", "sharpe": 1.85}
)

# Query similar successful strategies
similar = memory.query_similar_strategies(
    target_sharpe=1.80,
    n_results=5
)

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   API Layer     │────▶│  Strategy Engine│────▶│  Visualization  │
│   (FastAPI)     │     │  (Node Graph)   │     │  (Flowchart)    │
└─────────────────┘     └─────────────────┘     └─────────────────┘
         │                       │
         ▼                       ▼
┌─────────────────┐     ┌─────────────────┐
│   Risk Engine   │◀───▶│  Backtest Engine│
│ (Monte Carlo)   │     │ (Event-driven)  │
└─────────────────┘     └─────────────────┘
         │                       │
         ▼                       ▼
┌─────────────────┐     ┌─────────────────┐
│   Data Layer    │     │  Execution Layer│
│ (Provider API)  │     │ (Paper/Live)    │
└─────────────────┘     └─────────────────┘

Full API documentation available at /docs (Swagger UI) when server is running.

# Clone repository
git clone https://github.com/yourusername/intelligent-strategy-trading.git
cd intelligent-strategy-trading

# Install with development dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v --cov=ist

# Type checking
mypy src/

# Linting
ruff check src/
ruff format src/

• Core API foundation
• Visual strategy nodes
• Event-driven backtest engine
• Multi-factor risk modeling
• Monte Carlo path simulation
• Live broker integrations (Interactive Brokers, OANDA, Alpaca)
• Machine learning factor integration
• Prometheus + Grafana monitoring
• WebSocket real-time data
• Web-based visual editor

# Quick start
docker-compose up -d

# With PostgreSQL and Redis
docker-compose --profile production up -d

# Start Prometheus + Grafana
docker-compose --profile monitoring up -d

# Access dashboards
# Grafana: http://localhost:3000 (admin/admin)
# Prometheus: http://localhost:9090

Pre-configured Grafana dashboard "IST Trading - Overview" includes:

• System metrics (CPU, memory, threads)
• API metrics (request rate, P95 latency, error rate)
• Business metrics (order executions, latency)
• Risk metrics (drawdown, VaR, Sharpe ratio, active connections)

# Access API
curl http://localhost:8000/health

See docs/deployment.md for detailed deployment options.

MIT License - see LICENSE file.

Disclaimer: This software is for educational and research purposes. Trading financial instruments carries significant risk. Past performance does not guarantee future results.