✨Data Intelligence Lab@HKU✨

Detect five-point price patterns using Fibonacci ratios and generate buy, sell, or stand-aside signals. The skill identifies Gartley, Bat, Butterfly, and Crab patterns and locates potential reversal zones, where a price trend may change direction.

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• Detecting harmonic patterns in price data
• Identifying potential reversal zones
• Generating buy, sell, and stand-aside signals

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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• Testing whether an investment factor predicts returns
• Comparing factor strength across industries and markets
• Checking how predictive power changes with holding periods

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.

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• Estimate transaction costs for large orders
• Assess liquidity using order book depth and spreads
• Study trading imbalances and signs of liquidity stress

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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• Checking whether a dividend is sustainable
• Screening high-yield stocks for payout risks
• Comparing the quality of dividend growth

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.

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• Score the potential price impact of news and announcements
• Organize historical events into a standard CSV file
• Combine event assessments with price-based trading signals

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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• Building trading signals from price and volume data
• Comparing machine-learning models
• Testing expanding and sliding training windows

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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• Investigating backtest execution errors
• Finding why a strategy produces no trades
• Checking delayed trades or low capital usage

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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• Detect classic patterns in candlestick data
• Combine bullish and bearish patterns into one signal
• Adjust thresholds for candle bodies and shadows

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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• Find value stocks using valuation and profitability criteria
• Filter Chinese A-shares using published financial statements
• Build a US or Hong Kong stock universe for strategy testing

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.

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• Reviewing annual and quarterly financial performance
• Comparing risk disclosures with previous filings
• Investigating major events reported in 8-K filings

Interpret economic indicators and central bank policy across China, the United States, and Europe. Identify the economic cycle stage and produce a structured assessment of whether to increase, maintain, or reduce allocations to major asset classes.

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• Assess the economic cycle using growth and inflation data
• Interpret policy signals from the Fed, PBOC, and ECB
• Review allocation directions across major asset classes

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.

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• Compare earnings forecasts with analyst consensus
• Measure earnings surprises after results
• Track upward and downward analyst revisions

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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• Planning an eight-part company analysis series
• Checking figures and definitions across articles
• Revising unsupported claims before publication

Assess a company’s management through public statements, delivered commitments, crisis decisions, and use of company funds. Produce evidence-based scores for integrity, strategy, capital allocation, and governance, alongside a verdict on long-term trust.

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• Check whether executives delivered on past commitments
• Review acquisitions, buybacks, dividends, and new ventures
• Assess ownership, compensation, and related-party dealings

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.

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• Reviewing market optimism and fear
• Examining momentum and reversal strategies
• Checking investment decisions for cognitive biases

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.

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• Compare funds by returns, risk, and fees
• Detect changes in a fund's investment style
• Evaluate fund managers and ETF index tracking

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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• Check whether reported profits translate into cash
• Explain changes in return on equity
• Review accounting warning signs

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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• Choose price data sources for historical strategy testing
• Find tools for financial statements, news, and economic indicators
• Check required API keys and alternative data 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.

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• Detecting five-wave advances in price data
• Identifying completed ABC corrections
• Adjusting wave-detection parameters

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.

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• Estimate futures positions to offset broad market risk
• Compare the costs and protection of option strategies
• Plan protection against a severe market crash

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.

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• Building a backtest combining stocks and crypto
• Applying different indicator settings to each market
• Adjusting portfolio weights for differences in volatility

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.

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• Testing strategies on historical market data
• Comparing predefined predictive factors
• Reviewing trading habits from broker CSV exports

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.

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• Track overseas capital moving into and out of Chinese A-shares
• Review mainland investor flows into Hong Kong stocks
• Identify sector shifts in cross-border allocations

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.

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• Evaluate trends with the five Ichimoku lines
• Identify crossovers that agree with the cloud
• Adjust calculation periods and displacement