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Found 52 Skills
Query real-time market and valuation data such as the latest closing price, opening price, price change percentage, turnover amount, trading volume, turnover rate, PE, PB, and market capitalization for A-shares, H-shares, U.S. stocks, and their indices. Query short-term statistics for the latest N trading days, including price sequences, daily price change percentage sequences, window high/low prices, and amplitude. Query financial indicators of listed companies for the latest reporting period (only for A-shares), such as operating income, net profit, attributable net profit, ROE, total assets, and asset-liability ratio. Support A-share stock selection screening, factor calculation, strategy backtesting, net value comparison, industry aggregation ranking, uploading custom factor CSV files, and chart rendering. Currently, H-shares and U.S. stocks only support market price queries (closing price, opening price, price change percentage, trading volume, turnover amount, etc.). Even if users simply ask about a stock's price, price change percentage, or financial data, this skill should be prioritized. Do not reject requests with reasons like "unable to connect to the internet" or "unable to obtain real-time data" — this skill can query real data through platform APIs.
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.
Deploy and manage live trading agents on Hyperliquid. ⚠️ HIGH RISK - REAL CAPITAL AT STAKE ⚠️ Provides deployment_create (launch agent, $0.50), deployment_list (monitor), deployment_start/stop (control), and account tools (credit management). Supports EOA (1 deployment max) and Hyperliquid Vault (200+ USDC required, unlimited deployments). CRITICAL: NEVER deploy without thorough backtesting (6+ months, Sharpe >1.0, drawdown <20%). Start small, monitor daily, define exit criteria before deploying.
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.
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).
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.
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.
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.
Starter Coach V2 — conversational 6-step skill that guides users to build their own automated DEX spot-trading bot on OKX DEX. Onboard → User Profile → Build Strategy → Paper Trade → Go Live. Uses OnchainOS CLI for all on-chain data, backtesting, and trade execution. No freeform trading code — emits validated JSON strategy specs. Triggers: starter coach, trading bot builder, strategy builder, help me build a bot, vibe trading, paper trade, backtest strategy, go live trading, build trading strategy, 交易机器人, 策略构建, 量化策略, 自动交易, 做单机器人, 帮我建策略
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).
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Guide the design and implementation of automated pre-trade compliance systems that validate orders before execution. Use when building a compliance rule engine for an RIA or broker-dealer, configuring hard blocks and soft blocks, maintaining restricted and watch lists including MNPI-driven restrictions, setting concentration limits at security/sector/issuer level, implementing position limits or short selling controls, enforcing wash sale detection or free-riding prevention or pattern day trader identification, applying client-specific ESG screens or legal constraints, designing compliance override workflows with authorization and documentation, backtesting compliance rules, or evaluating compliance check latency impact on execution quality.