Claude Trading Skills

Detects Follow-Through Day (FTD) signals for market bottom confirmation using William O'Neil's methodology, with dual-index tracking and a state machine for rally attempt, FTD qualification, and post-FTD health monitoring.

Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP) and detect historical VCPs in a single ticker's price path.

Screen post-earnings gap-up stocks for Post-Earnings Announcement Drift patterns using weekly candle analysis to detect red candle pullbacks and breakout signals.

Synthesize market breadth, regime, and flow signals into a unified capital commitment recommendation with exposure ceiling, bias, and action guidance.

Calculate risk-based position sizes for long stock trades using fixed fractional, ATR-based, or Kelly Criterion methods with portfolio constraints.

Analyzes sector rotation patterns and market cycle positioning using publicly available CSV data, with optional chart image analysis.

Generates a structured Claude CLI prompt from a JSON idea specification to bootstrap a complete skill directory with SKILL.md, references, scripts, and tests following repository conventions.

Detects and ranks trending market themes by analyzing cross-sector momentum, volume, and breadth signals with lifecycle maturity assessment.

Provides expert guidance for systematic backtesting of trading strategies, covering methodology, stress testing, bias prevention, and result interpretation.

Translate natural-language stock screening requests into FinViz screener filter codes, build the URL, and open it in Chrome.

Screen US stocks using William O'Neil's CANSLIM growth stock methodology with composite scoring and weighted components.

Extract, score, and backlog skill idea candidates from Claude Code session logs for a weekly skill generation pipeline.

Analyzes market breadth using Monty's Uptrend Ratio Dashboard data to diagnose the current market environment. Generates a 0-100 composite score from 5 components (breadth, sector participation, rotation, momentum, historical context).

Retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep API, filtered by market cap over $2B, and organizes them into a markdown table grouped by date and timing.

Analyze investment portfolios by fetching real-time holdings via Alpaca MCP Server, then assess asset allocation, diversification, risk metrics, and generate rebalancing recommendations.

Analyzes 18-month investment scenarios from a news headline by invoking two specialized agents and generating a comprehensive report with sector impacts, stock recommendations, and a critical review.

Record and analyze post-trade outcomes for trading signals, tracking false positives, missed opportunities, and regime mismatches to generate feedback for signal aggregation and skill improvement.

Analyze weekly price charts for stocks, indices, crypto, or forex to identify trends, support/resistance, and probabilistic scenarios based purely on chart data.

Analyze US stocks with fundamental and technical analysis, peer comparisons, and investment reports using real-time market data.

Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis.

Convert daily market observations and news reactions into structured edge hints, with optional LLM augmentation, outputting a canonical hints.yaml for downstream concept synthesis.

Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure covering screening, deep dive, entry planning, and post-purchase monitoring.

Analyzes recent market-moving news events and their impact on US equity markets and commodities, producing impact-ranked reports using web search and fetch tools.

Quantifies market top probability using O'Neil Distribution Days, Minervini leading stock deterioration, and Monty defensive sector rotation, producing a 0-100 composite score with risk zone classification for tactical 2-8 week timing signals.

Identifies and analyzes statistical arbitrage opportunities through pair trading, using correlation analysis, cointegration testing, and z-score calculations to generate entry/exit signals for market-neutral strategies.

Validate financial data quality in market analysis documents before publication, checking price scales, instrument notation, date accuracy, allocation totals, and unit usage.

Convert daily market observations into structured research tickets and export validated candidate specs for a trading strategy pipeline.

Scans US equities for daily +20%/-20% movers, classifies catalysts and chart context, updates forward outcomes, and summarizes cohort patterns for research.

Analyze S&P 500 market breadth charts and CSV data to assess market health, identify trading signals, and generate positioning recommendations for different trader types.

Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis, analyzing RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation to identify regime shifts.

Generate Minervini-style breakout trade plans from VCP screener output with risk calculation, portfolio heat management, and Alpaca-compatible order templates.

Aggregate and rank signals from multiple edge-finding skills into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

Converts abstract edge concepts into concrete strategy draft variants with configurable risk profiles and optional exportable ticket YAMLs for downstream validation.

Reviews trading strategy drafts for edge plausibility, overfitting risk, sample size adequacy, and execution realism, outputting PASS/REVISE/REJECT verdicts with confidence scores.

Screens recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position) and assigns A/B/C/D grades to identify momentum trade candidates.

Quantifies market breadth health using public CSV data, generating a 0-100 composite score across six components to assess market participation and rally breadth.

Detect when backtest iteration has stalled and generate structurally different strategy pivot proposals to break out of local optima.

Reviews closed trades, partial exits, and monthly trade aggregates for process adherence, risk discipline, execution quality, and evidence-based trading behavior patterns.

Screen US stocks for high-quality dividend opportunities combining value characteristics, attractive yields, and consistent growth using a two-stage screening approach with FINVIZ Elite and FMP APIs.

Evaluates account-level drawdown and losing-streak rules from local thesis files to decide whether new trade risk is allowed, without external APIs.

Review AI agent skills using a dual-axis method: deterministic code-based checks and LLM deep review, with weighted scoring and improvement recommendations.

Clusters raw detection tickets into reusable edge concepts with thesis, invalidation signals, and strategy playbooks before strategy design.

Analyze and simulate options trading strategies using Black-Scholes pricing, Greeks calculation, and risk management guidance.

Validate multi-skill workflows by checking skill existence, inter-skill data contracts, JSON schema compatibility, file naming conventions, and handoff integrity.

Generate falsifiable trade strategy hypotheses from market data, trade logs, and journal snippets, with ranked hypothesis cards, experiment designs, kill criteria, and optional strategy.yaml export.

Routes trading and investing goals to the right workflow, skillset, API profile, and setup path without executing trades.

Fetch upcoming economic events and data releases using the FMP API, including central bank decisions, employment reports, inflation data, GDP releases, and other market-moving indicators for specified date ranges.

Evaluates a local pre-trade checklist before manual order entry, blocking planless, oversized, revenge-risk, market-regime-blocked, or circuit-breaker-blocked entries while journaling the decision for later review.

Evaluates US market bubble risk through quantitative data analysis using the Minsky/Kindleberger framework, scoring indicators like Put/Call ratio, VIX, margin debt, breadth, and IPO data.

Aggregate closed trades from trader-memory-core into a weekly performance report with win rate, expectancy, profit factor, R-multiple, MAE/MFE, and pattern breakdowns by source skill, exit reason, sector, and mechanism.

Coordinate multi-stage edge research pipelines from candidate detection through strategy design, review, revision, and export.

Tracks institutional investor ownership changes and portfolio flows using 13F filings data to identify stocks with significant smart money accumulation or distribution.

Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.

Analyzes global market conditions across US, European, and Asian indices, forex, commodities, and economic indicators to produce structured market reports with risk sentiment assessment.

Detect IBD-style Distribution Days for QQQ/SPY, track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk, and emit TQQQ/QQQ exposure recommendations.

Screen US equities for parabolic exhaustion patterns, generate conditional pre-market short plans, and monitor intraday trigger signals using 5-min bars.

Monitor dividend portfolios for risk triggers and route anomalies into a human review queue without auto-selling.

Build and maintain a model book for Stockbee-style Momentum Burst setups by ingesting screener candidates, updating 3-day and 5-day forward outcomes with MFE/MAE and stop-hit status, and summarizing cohort statistics to improve setup recognition.

Synthesizes outputs from 8 upstream market analysis skills into a unified conviction score (0-100), pattern classification, and allocation recommendation for Druckenmiller-style portfolio positioning.

Analyzes Stockbee-style Day 1 Episodic Pivot candidates by scoring catalyst quality alongside price/volume confirmation, gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low.