30 件 ・ 関連度順
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Apply backtesting frameworks in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses backtesting frameworks or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Build algorithmic trading systems — backtesting, strategy development, live execution, and risk management. Use when tasks involve backtesting trading strategies, connecting to exchange APIs (Binance, Alpaca, Interactive Brokers), implementing technical indicators, portfolio optimization, order execution, risk management rules, market data processing, or building trading bots. Covers both crypto and traditional markets.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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 strateg...
日本語の概要は準備中です。原文の説明を表示しています。
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
日本語の概要は準備中です。原文の説明を表示しています。
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
日本語の概要は準備中です。原文の説明を表示しています。
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
日本語の概要は準備中です。原文の説明を表示しています。
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
日本語の概要は準備中です。原文の説明を表示しています。
Develop AxonX research plugins and operate quantitative research tasks through CLI or MCP, inspecting execution status, logs, artifacts, and lineage.
日本語の概要は準備中です。原文の説明を表示しています。
Strategy Development Manager: convert academic papers and research reports into validated factors and strategies with automated backtesting, persistent storage, and decay monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
Options strategy framework supporting Black-Scholes pricing, Greeks analysis, and multi-leg backtesting. Suitable for cryptocurrency and equity options.
日本語の概要は準備中です。原文の説明を表示しています。
Factor research framework with IC/IR analysis, quantile backtesting, and factor combination. Suitable for cross-sectional factor evaluation across multiple instruments.
日本語の概要は準備中です。原文の説明を表示しています。
Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).
日本語の概要は準備中です。原文の説明を表示しています。
Minute-level data analysis and backtesting. Retrieves minute candlesticks through OKX/Tushare/yfinance and can be used both for analysis and as input to the backtest engine.
日本語の概要は準備中です。原文の説明を表示しています。
Separate a strategy return series into declared baseline exposure and residual edge with returns-based OLS attribution, HAC inference, rolling stability, alternate-baseline sensitivity, and regime breakdowns. Use when evaluating whether backtest, out-of-sample, or live returns contain independent alpha beyond market, equal-weight, momentum, sector, or user-supplied factor returns; when explaining whether a drawdown came from baseline exposure or strategy-specific behavior; or when a strategy needs an attribution quality gate after backtesting. Do not use for holdings-based Brinson attribution, feature-level Shapley explanations, or analysis from summary metrics without a dated return series.
日本語の概要は準備中です。原文の説明を表示しています。
Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.
日本語の概要は準備中です。原文の説明を表示しています。
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics
日本語の概要は準備中です。原文の説明を表示しています。
Use when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and falsification protocols distilled from a real live-money campaign
日本語の概要は準備中です。原文の説明を表示しています。
Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators
日本語の概要は準備中です。原文の説明を表示しています。
Venue- and market-type-agnostic strategy, sizing, and backtesting layer for binary prediction markets (Kalshi, Polymarket, ForecastEx). Covers the durable edge thesis, fee-aware selection, fractional-Kelly sizing, and leak-free validation methodology.
日本語の概要は準備中です。原文の説明を表示しています。
Financial trading CLIs — spot and perp trading on Hyperliquid, Binance, Coinbase, OKX. Prediction markets on Polymarket. Deposit and withdraw across chains. LLM-enriched price quotes with trend analysis. News and SEC filings. Historical backtesting with forward PnL analysis. Use when: user asks about stock/crypto prices, wants to trade, deposit, withdraw, check portfolio, or backtest a strategy.
日本語の概要は準備中です。原文の説明を表示しています。