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Build, optimize, validate, and export quant strategies through MCP.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "alpha-forge-mcp" yet — see the docs or source repo.
Create a JSON quant strategy for AlphaForge: use the 15-minute timeframe, go long on EMA crossover with RSI filter, and include entry, stop-loss, take-profit, and position sizing rules.
A usable AlphaForge strategy JSON draft with complete trading logic and parameters.
Use Optuna TPE to optimize this AlphaForge strategy's EMA periods, RSI thresholds, and stop-loss/take-profit settings, then return the best parameter set and key performance metrics.
The best parameter configuration plus a summary of return, drawdown, Sharpe, and other core backtest metrics.
Run walk-forward validation on the current strategy to assess overfitting; if the results are acceptable, export it to TradingView Pine Script v6.
A walk-forward validation summary with metrics, plus a Pine Script v6 file ready for TradingView.
Discover indicators, build strategies, run backtests, and analyze results conversationally.
Let AI read a MetaTrader 5 account and place trades with approval.
Connect AI to real trading infrastructure for data, backtests, signals, and execution.
Manage Freqtrade bots, strategies, balances, and backtests from any MCP agent.
Run quantitative strategy backtests and analyze results through the Backtest360 engine.
Find and compare MCP servers for AI finance agent workflows.