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Found 87 Skills
Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.
Test trading strategies on historical data to evaluate performance, risks, and profitability.
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Academic backtesting framework for quantitative research. ~30 risk and performance ratios, 10 classes of indicators, event-driven engine with 6+ strategies, MPT optimizer, forward-looking simulation with Johnson SU + t-Copula, walk-forward CV, stress testing, fundamental analysis (Altman Z, Piotroski, DuPont). All flat Python + numpy.
Quickly fetch data and print key backtest stats for a symbol with a default EMA crossover strategy. No file creation needed - runs inline in a notebook cell or prints to console.
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
Design, validate, and recalibrate a lead scoring model - fit and engagement signals, weighting, negative scoring and exclusions, decay, MQL/PQL threshold and tier setting, backtesting against closed-won/closed-lost outcomes, and governance; also diagnoses an existing broken model. Use whenever the user mentions lead scoring, an MQL threshold, fit vs engagement scoring, product-qualified leads, score decay, "score my leads", "our lead scores are wrong", "sales rejects our MQLs", or "too many junk MQLs" - even if they never say "scoring model". Covers B2B sales-led and B2C/PLG, rules-based design and predictive readiness. Do NOT use for assigning scored leads to reps - use mbfinotti/revops-skills@lead-routing instead.
Build, test, and deploy DeFi trading strategies using the Almanak SDK. ALWAYS use this skill when the user mentions almanak, DeFi strategy, trading strategy, yield farming, liquidity provision, token swap, borrowing, lending, perpetuals, staking, vault deposit, bridging tokens, backtesting, paper trading, or on-chain execution. Use for writing strategy.py files, composing intents (Swap, LP, Borrow, Supply, Perp, Bridge, Stake, Vault, Prediction), working with config.json strategy parameters, running almanak strat or almanak gateway CLI commands, or debugging strategy execution on Anvil forks. Do NOT use for general smart contract development, Solidity code, or non-strategy SDK internals.