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.
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.
Turn news, company announcements, and policy events into scored trading inputs. Store assessments in a standard CSV file, reduce their influence over time, and combine them with signals from price indicators to support event-based strategy testing.
Compare company earnings forecasts with analyst consensus, the market’s shared estimate. Assess earnings surprises and changes in analyst expectations, then organize signals and supporting assumptions for trading research and historical testing.
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.
Research stock and cryptocurrency strategies through historical testing, factor analysis, and options pricing. Analyze trading journals, extract rules from profitable trades, and generate reports comparing simulated results with actual trading outcomes.
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.
Generate trading signals using Ichimoku Kinko Hyo, a five-line chart framework. Combine conversion-line and base-line crossovers with price position and cloud direction to classify conditions as long, short, or stand aside using pandas.
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.
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.
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.
Design research plans to protect existing investments using futures, ETFs, options, or other assets. Compare hedge sizes, estimated costs, and potential gains or losses under different market scenarios, with guidance on monitoring and adjustments.
Analyze cross-border stock flows between mainland China and Hong Kong through Stock Connect. Compare overseas buying of mainland shares, mainland buying of Hong Kong shares, sector allocations, and price gaps between dual-listed shares.
Evaluate investment funds and ETFs using returns, risk, fees, investment style, and manager performance. Detect changes in style, compare how closely ETFs follow their indexes, and plan portfolios that invest in multiple funds.
Analyze bid–ask spreads, order books, and buying and selling imbalances to assess liquidity, or how easily an asset trades. Estimate large-order price impact and transaction costs, with additional guidance on Chinese A-share auction and block trades.
Write signal generation code for backtests combining stocks, crypto, forex, and other markets. Apply market-specific indicator settings and volatility-adjusted weights, while the existing CompositeEngine handles trading calendars, shared capital, and each market's rules.
Analyze bond prices, yields, and issuers’ ability to repay debt. The skill covers interest rate exposure, default risk models, yield spreads, Chinese local government financing platform bonds, and securities backed by pools of loans.
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.
Explore bundled libraries of investment factors, signals used to compare securities, and inspect individual formulas and metadata. Evaluate a factor or an entire library across a chosen universe and period, producing aggregate performance statistics and an HTML report.
Analyze US public company filings to summarize financial health, management commentary, changing risks, insider trades, and institutional holdings. Combine these findings into a structured research report with scores and investment implications.
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.
Use investor psychology to examine trading signals and risk controls, especially in China A-shares. The skill provides sentiment measures, checklists for biased decisions, and frameworks for assessing momentum, which follows trends, and reversal strategies.
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.
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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