Translate a Python trading strategy or written trading rules into code for TradingView, 通达信, 同花顺, 东方财富, and MT5. Receive platform-specific indicators or strategy code, setup instructions, and notes explaining features that cannot be translated directly.
Assess market sentiment using fear and greed measures, options activity, margin trading, foreign investment flows, and social media discussion. Combine indicators on a common scale to produce a dashboard and explain signs of excessive optimism or fear.
Review trading rules across China A-shares, Hong Kong stocks, US stocks, and crypto markets. Use comparison tables and checklists to identify execution limits, short-selling constraints, trading hours, fees, and tax considerations when designing historical trading simulations.
Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.
Generates buy, sell, or stand-aside signals from calendar patterns using price and volume data. Supports configurable months and weekdays, including combined conditions, for testing seasonal trading ideas against historical data.
Collect financial discussions from Twitter/X, Telegram, Discord, and Reddit, then measure sentiment and discussion volume. Use the resulting data to study market attention, distinguish author groups, and test whether social signals relate to future returns.
Build a pair trading strategy for two related instruments using their price ratio. Measure how far the ratio moves from its recent average, generate opposite buying and selling signals, and close positions when the ratio returns near that average.
Finds and retrieves paid financial datasets when free sources lack coverage or detail. Guides comparison of providers, success rates, prices, and required inputs, while keeping execution costs and remaining credits visible.
Analyze crypto perpetual futures funding payments and spot–futures price differences to assess carry trades. Compare exchanges, identify crowded positions, and summarize potential returns alongside risks such as funding reversals and liquidation.
Generate trading signals from historical volatility, a measure of past price fluctuations. Rank its current level against recent history, signal long positions at low levels, and exit or short at high levels while retaining positions between thresholds.
Evaluate companies using projected cash flows, dividends, business segments, and comparisons with peers. Select suitable valuation methods, examine how assumptions change estimated value, and check for problems that can make a seemingly cheap stock misleading.
Document why you own a stock, the assumptions behind the purchase, and conditions for reconsidering it. Review these against quarterly earnings and major events, score the investment rationale's health, and outline whether to hold, add, trim, or exit.
Retrieve cryptocurrency prices, candlesticks, order books, funding rates, and open interest from OKX’s public API. Use spot, derivatives, and index data for market research without registering an account or configuring authentication.
Compare money moving into and out of US exchange-traded funds to assess sector rotation and appetite for risk. Produce a structured view of broad market, growth versus value, bond, and thematic fund flows.
Generate trading signals using Smart Money Concepts, a framework for interpreting institutional trading behavior. Analyze trend continuation, reversals, price gaps, and order concentration zones, then combine structural signals and gaps to determine a trading direction.
Turn trading rules into Python code and configuration files, then test them against historical data. Review performance, position sizing, and trading costs, and revise strategies using the results produced by the backtest engine.
Backtest stock and cryptocurrency option strategies using theoretical prices derived from underlying prices. Simulate combinations of option positions, track profit and loss, and export Greeks, measures of sensitivity to price, volatility, and time.
Analyze scheduled token releases, their recipients, and trading volume to estimate potential selling pressure. Review project treasury holdings, operating runway, and token supply growth to assess funding sustainability and the effect of dilution on staking yields.
Retrieve U.S. company filings and financial series from SEC EDGAR. Resolve stock tickers to company identifiers, list recent reports with document links, and fetch reported metrics such as revenue or assets without an API key.
Retrieve price and trading volume data for US, Hong Kong, and Canadian stocks, ETFs, and indices through Yahoo Finance. Also gather company profiles, financial statements, dividends, and ownership data for market research and historical strategy testing.
Check investment research for bias before searching and before writing conclusions. A short checklist broadens coverage beyond large companies and English sources, challenges bullish assumptions, and checks whether important figures are current.
Analyze price and volume data using trend indicators, overbought and oversold measures, and volume changes. Combine these three perspectives into a single long, short, or stand-aside signal, with adjustable calculation periods and thresholds.
Provides guidance for retrieving China A-share prices and trading volumes through the TDX protocol. The source is currently retired from automatic routing because servers stopped responding, with explicit selection retained for possible future recovery.
Convert a Vibe-Trading backtest strategy or a written trading strategy into a Python class for vnpy. Map indicators and trading signals to vnpy conventions, with asset-specific order handling and instructions for loading the strategy for backtesting or live trading.
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