“numpy”的搜索结果

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

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

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

Compare exchange-traded funds by fees, tracking accuracy, liquidity, and market-price differences from asset value. Use the China-focused framework and Python examples to screen products, examine sector rotation, and study portfolio allocation.

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• Comparing ETFs that track the same index
• Estimating the long-term impact of fees
• Checking cross-border ETF premiums

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

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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• Analyzing cryptocurrency price movements with minute candles
• Calculating volume-weighted and time-weighted average prices
• Comparing trading volume by hour

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 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

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

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

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

Monitor requests to an AI prediction API for signs of model copying or training-data exposure. Test an authorized model with simulated attacks, measure the risk, and evaluate defenses such as request limits and reduced prediction detail.

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• Monitoring a public or partner prediction API
• Measuring model copying risk before deployment
• Testing request limits and reduced prediction detail

Analyze DNS logs to find signs of hidden data transfers. Use query length, character randomness, traffic volume, and response sizes to identify suspicious domains and support monitoring rules with Python, Suricata, and Splunk examples.

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• Investigating possible data theft through DNS traffic
• Ranking suspicious domains found in DNS logs
• Creating DNS tunneling detection rules

Examine recorded calls and voicemails for signs of AI-generated speech. Extract acoustic features, classify samples with machine learning, and prepare reports with confidence scores and sound visualizations to support voice impersonation investigations.

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• Investigating calls impersonating executives
• Checking suspicious voicemails
• Comparing suspect recordings with genuine voice samples

Examine Zeek connection logs for regular callbacks that may indicate communication with an attacker’s control server. Use ZAT and Pandas to group connections, measure timing variation, and identify source and destination pairs for further investigation.

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• Hunting for periodic callbacks in Zeek connection logs
• Comparing connection timing across source and destination pairs
• Developing beaconing detection rules and hunting queries

Build normal traffic profiles for industrial control networks and identify unusual connections, polling intervals, and protocol operations. Use machine learning and process records to investigate deviations and produce anomaly reports with severity levels.

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• Establishing normal traffic profiles for industrial networks
• Detecting unusual connections and polling intervals
• Investigating Nozomi Guardian or Dragos alerts

Analyze existing user permissions to propose roles for role-based access control, which assigns permissions by job role. Compare candidate role sets, identify overlap and excess access, and validate definitions against business responsibilities.

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• Consolidating overlapping access roles
• Redesigning roles from existing permission data
• Reviewing excess and unusual permissions

Create animated GIFs for Slack emoji and messages using drawn shapes or uploaded images. Add motion such as bouncing, rotation, and fading, adjust dimensions and colors, and validate the finished file against Slack requirements.

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• Create animated reaction emoji for Slack
• Animate an uploaded image
• Make a bouncing or rotating GIF for a message

Build similarity search with FAISS to find nearby matches in large collections of numeric vectors. The skill guides index selection, memory reduction, GPU use, and saving indexes while balancing search speed and accuracy.

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• Find similar items in large vector datasets
• Choose an index for search speed and accuracy
• Reduce memory use for vector search

Check single-cell RNA-seq data quality, filter low-quality cells using median absolute deviation, a measure of spread, and compare plots before and after filtering. Outputs include filtered data and the original dataset with quality annotations.

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• Assess single-cell RNA-seq data quality
• Filter low-quality cells and rarely detected genes
• Compare quality plots before and after filtering

Analyze users grouped by signup period or another shared characteristic to compare retention and feature adoption over time. Produce charts, highlight unusual changes, and recommend follow-up research or experiments to investigate the patterns.

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• Comparing retention across signup months
• Tracking adoption of a new feature by user group
• Finding periods with unusual user drop-off

Helps explore datasets, clean missing or inconsistent values, and explain patterns through statistics and charts. Uses Python and SQL to compare groups, examine relationships, and present findings in plain language with documented analysis steps.

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• Checking missing values, duplicates, and inconsistent formats
• Exploring trends and distributions with charts
• Comparing groups with statistical tests

Create animated GIFs for Slack messages and custom emoji from a description. Combine motion such as bouncing, spinning, and shaking, then check dimensions and file size and adjust frames or colors to fit the intended use.

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• Create animated emoji for reactions
• Make short GIFs for Slack messages
• Reduce GIF size for the intended use

Diagnose errors and incorrect inference results in MNN's QNN backend for Qualcomm HTP processors. Compare intermediate calculation data against CPU results to locate the first faulty operator, distinguish bugs from precision limits, and guide operator support and performance fixes.

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• Investigate QNN runtime and model conversion errors
• Find the first operator producing incorrect results
• Distinguish implementation bugs from numerical precision limits