Assess whether a Chinese A-share company may receive ST or *ST risk warnings next financial year. Review financial forecasts, dividends, audit opinions, and regulatory penalties, then report risk severity separately from confidence in the prediction.
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Use CCXT, a library with a common interface for over 100 cryptocurrency exchanges, to retrieve historical prices and volumes, current quotes, order books, and recent trades. Public market data requires no API key.
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Break down major industrial trends into materials, components, and equipment to find listed suppliers at supply bottlenecks. Compare scarcity, financial performance, and valuation, then organize candidates into a ranked research board with supporting evidence and risks.
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Analyze income statements, balance sheets, and cash flow statements together to assess earnings quality. Break down return on equity into its drivers, compare profits with operating cash flow, and organize warning signs that warrant closer accounting review.
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Analyze candlestick data using Chan theory, a method based on price structure. Detect turning points, movement segments, and overlapping price zones, then generate buy and sell signals and compare patterns across timeframes.
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Recognize classic candlestick patterns using pandas, a Python library for tabular data. Combine bullish and bearish pattern scores into long, short, or stand-aside signals, with adjustable thresholds for candle bodies and shadows.
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Track when assets begin moving together, assess the resulting loss of diversification, and examine which assets moved first during a crisis. Rank changes in asset relationships and report uncertainty explicitly for risk monitoring and retrospective analysis.
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Choose financial data sources and tools for price history, financial statements, news, and economic research. The skill maps market coverage, credentials, access limits, and fallback options, and provides rules for checking important figures against independent sources.
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Plan portfolio allocations across assets using risk, correlations, return forecasts, and trading costs. Compare five optimization methods, produce settings for config.json, and define rebalancing rules that adjust holdings back toward their target weights.
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Screen Chinese A-shares, Hong Kong stocks, and US stocks using valuation ratios, profitability, and financial statement data. Set criteria to identify value or growth candidates, with market-specific guidance on using the results in historical strategy tests.
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Interpret crypto liquidation heatmaps, which show estimated price levels where leveraged positions may be forcibly closed. Organize major clusters, compare concentrations above and below the current price, and assess cascade risks and potential support or resistance zones.
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Analyze convertible bonds in China's A-share market through bond value, stock conversion value, and option value. Review conversion-price resets and redemption clauses, then compare candidates using low-price, low-premium screening and periodic portfolio rotation frameworks.
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Analyze price swings to detect Elliott Wave structures: five waves following a trend and three waves correcting it. Check wave rules and Fibonacci ratios, then generate signals for completed advances or pullbacks using a pandas implementation.
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Model trading costs and execution assumptions in backtests, including slippage, the gap between expected and simulated fill prices. Compare price impact, fees, execution delays, and order splitting to assess how assumptions affect strategy results.
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Analyze crypto derivatives for research and backtesting using perpetual funding payments, futures prices across expiry dates, and options volatility. Organize market metrics, compare strategy candidates, and outline risks such as margin shortages, funding reversals, and exchange failures.
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Retrieve minute-level price data for stocks and cryptocurrencies, calculate average prices and volume patterns, and test intraday trading strategies. Configure the candle interval and trading costs to examine results using historical market data.
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Build machine-learning trading signals from price and volume data. The workflow checks inputs, creates predictive features, and repeatedly trains on available historical data to produce bounded signals indicating predicted direction and strength.
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Access stock fund flows, disclosures, news, financial statements, and market rankings through Vibe-Trading tools. The skill documents available data interfaces and examples, including symbol lookup, with market-specific sources and no token required.
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Compare yields from decentralized finance lending, liquidity provision, and staking. Assess returns alongside price-related losses, smart contract risks, and dependence on token rewards, then organize opportunities by risk and the sustainability of their income sources.
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Develop an eight-part article series examining one company's competitive strengths, earnings, assets, management, and valuation. Review figures, sources, accounting definitions, and claims across the series to prepare consistent articles for public publication.
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Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.
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Evaluate dividend stocks by checking earnings, cash flow, debt, and dividend history. Assess whether payouts can last, identify high yields driven by falling prices, and consider total returns alongside dividend income.
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Evaluate investment factors across multiple securities using statistical measures and grouped backtests. Compare how well each factor predicts future returns, examine its consistency over time, and combine useful factors with equal or performance-based weights.
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Extract text from PDFs, Office documents, images, spreadsheets, and text files through one tool. Results share a common JSON structure, supporting document summaries, contract checks, and spreadsheet previews, with optical character recognition for scanned content.
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