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Found 67 Skills
Use when building trading systems, backtesting strategies, implementing execution algorithms, or analyzing market microstructure - covers strategy development, risk management, and production deploymentUse when ", " mentioned.
Build trading systems in the style of Two Sigma, the systematic investment manager pioneering machine learning at scale. Emphasizes alternative data, distributed computing, feature engineering, and rigorous ML infrastructure. Use when building ML pipelines for alpha research, feature stores, or large-scale backtesting systems.
World-class systematic trading research - backtesting, alpha generation, factor models, statistical arbitrage. Transform hypotheses into edges. Use when "backtest, alpha, factor model, statistical arbitrage, quant research, systematic trading, mean reversion, momentum strategy, regime detection, walk forward, " mentioned.
Comprehensive guide for FinLab quantitative trading package for Taiwan stock market (台股). Use when working with trading strategies, backtesting, Taiwan stock data, FinLabDataFrame, factor analysis, stock selection, or when the user mentions FinLab, trading, 回測, 策略, 台股, quant trading, or stock market analysis. Includes data access, strategy development, backtesting workflows, and best practices.
Use Robonet's MCP server to build, backtest, optimize, and deploy trading strategies. Provides 24 specialized tools for crypto and prediction market trading: (1) Data tools for browsing strategies, symbols, indicators, Allora topics, and backtest results, (2) AI tools for generating strategy ideas and code, optimizing parameters, and enhancing with ML predictions, (3) Backtesting tools for testing strategy performance on historical data, (4) Prediction market tools for Polymarket trading strategies, (5) Deployment tools for live trading on Hyperliquid, (6) Account tools for credit management. Use when: building trading strategies, backtesting strategies, deploying trading bots, working with Hyperliquid or Polymarket, or enhancing strategies with Allora Network ML predictions.
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Framework for developing, testing, and deploying trading strategies for prediction markets. Use when creating new strategies, implementing signals, or building backtesting logic.
Backtest trading strategies on historical data and interpret performance metrics. Provides run_backtest (crypto strategies) and run_prediction_market_backtest (Polymarket strategies). Fast execution (20-60s), minimal cost ($0.001). Returns Sharpe ratio, max drawdown, win rate, profit factor, and trade statistics. Use this skill after building or improving strategies to validate performance before deploying. NEVER deploy without thorough backtesting (6+ months recommended).
Implements comprehensive backtesting capabilities for Pine Script indicators and strategies. Use when adding performance metrics, trade analysis, equity curves, win rates, drawdown tracking, or statistical validation. Triggers on "backtest", "performance", "metrics", "win rate", "drawdown", or testing requests.
AI Trading Intelligence — live prices, 30+ technical indicators, backtesting (6 strategies), walk-forward overfitting detection, trade logs, equity curves, Reddit sentiment, news, and multi-market screener. Supports stocks, crypto, ETFs, indices, Turkish (BIST), and Egyptian (EGX) markets.
Pricing completo de opciones europeas y americanas. 9 metodos: Black-Scholes, Binomial CRR, Trinomial, Monte Carlo (antithetic) + Longstaff-Schwartz, Bjerksund-Stensland 2002 / BAW (American closed-form), Heston 1993 (vol estocastica, sonrisa via Fourier), Bates 1996 (Heston + Merton jumps, crash risk), greeks (BS), implied vol, P(ITM) y P(Profit). Disenado para backtesting: cada funcion es flat Python vectorizado con numpy (sin abstracciones), usa math.erfc (no scipy). BS 2.4 us/op, BS2 3.6 us, Heston 400 us, Binomial N=500 5.6 ms. CLI con 15 modos mas validate y bench. Time complexity O(1) para todos los closed-form.
This skill should be used when the user asks about writing trading strategies, backtesting, deploying Freqtrade bots, quantitative trading, strategy optimization, or any Freqtrade-related operation. Use when user says: 'write strategy', 'create strategy', 'backtest', 'deploy Freqtrade', 'deploy bot', 'quantitative trading', 'strategy optimization', 'hyperopt', 'live trading bot', '写策略', '创建策略', '回测', '部署Freqtrade', '部署机器人', '量化交易', '量化策略', '策略优化', '超参数优化', '实盘机器人'. IMPORTANT: ALWAYS use create_strategy to generate strategy files. NEVER write Python strategy code by hand. For crypto prices/charts, use aicoin-market. For exchange trading, use aicoin-trading. For Hyperliquid, use aicoin-hyperliquid.