跳到正文
cccskills
免费在 GitHub 公开

ibd-distribution-day-monitor

Monitor QQQ and SPY for falling prices accompanied by higher trading volume. Track active warning days, classify market risk into four levels, and generate daily reports suggesting TQQQ or QQQ exposure adjustments without executing trades.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Detect IBD-style Distribution Days for QQQ/SPY (close down at least 0.2% on higher volume), track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk (NORMAL/CAUTION/HIGH/SEVERE), and emit TQQQ/QQQ exposure recommendations. Use after market close, before TQQQ exposure changes, or as input to FTD/market-state frameworks. Does not execute trades.

实测评分 未实测

查看安装方法

适用场景

  • Review market risk after the US market closes
  • Check risk before increasing TQQQ exposure
  • Assess warning clusters during an uptrend
  • Supply signals to market-state analysis

包含的文件(28)

  • SKILL.md5.1 KB
  • config/default.yaml1.1 KB
  • references/ibd_distribution_methodology.md4.4 KB
  • references/tqqq_exposure_policy.md2.5 KB
  • requirements.txt140 B
  • scripts/_fmp_compat.py5.8 KB
  • scripts/data_loader.py3.0 KB
  • scripts/distribution_day_tracker.py5.9 KB
  • scripts/exposure_policy.py2.2 KB
  • scripts/fmp_client.py20.3 KB
  • scripts/history_utils.py1.9 KB
  • scripts/ibd_monitor.py16.4 KB
  • scripts/math_utils.py1.5 KB
  • scripts/models.py2.3 KB
  • scripts/report_generator.py5.8 KB
  • scripts/risk_classifier.py1.9 KB
  • scripts/tests/conftest.py315 B
  • scripts/tests/helpers.py3.4 KB
  • scripts/tests/test_data_loader.py3.4 KB
  • scripts/tests/test_distribution_day_tracker.py14.1 KB
  • scripts/tests/test_exposure_policy.py2.3 KB
  • scripts/tests/test_fmp_client_et.py2.5 KB
  • scripts/tests/test_fmp_client_transport.py3.3 KB
  • scripts/tests/test_history_utils.py2.0 KB
  • scripts/tests/test_ibd_monitor_coverage.py50.2 KB
  • scripts/tests/test_math_utils.py1.1 KB
  • scripts/tests/test_report_generator.py3.1 KB
  • scripts/tests/test_risk_classifier.py2.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

IBD Distribution Day Monitor

Purpose

Detect IBD-style Distribution Days for major market ETFs (QQQ as Nasdaq proxy, SPY as S&P 500 proxy) and produce a daily market deterioration signal plus a TQQQ/QQQ exposure recommendation. Designed for post-market review.

When to Use

Invoke this skill:

  • Daily after the US market close.
  • Before increasing TQQQ exposure or rebalancing leveraged positions.
  • When evaluating whether an uptrend is becoming vulnerable to a correction.
  • As an upstream input to FTD (Follow-Through Day) detection or other market-state frameworks.

Do NOT use this skill to:

  • Execute trades or modify orders.
  • Generate discretionary market predictions outside of the IBD ruleset.

Inputs

  • Symbols (default: QQQ, SPY) and lookback (default 80 trading sessions).
  • Optional --as-of YYYY-MM-DD for backtesting against a historical session.
  • Strategy context: instrument (TQQQ or QQQ), current exposure %, base trailing stop %.
  • FMP API key via --api-key, config.data.api_key, or FMP_API_KEY env var (in that priority order).

Core Rules

A Distribution Day is detected when:

  1. Today's close is at least 0.2% below yesterday's close.
  2. Today's volume is greater than yesterday's volume.

A Distribution Day is removed from the active count when either:

  • More than 25 trading sessions have elapsed since the DD.
  • The index has gained 5% from the DD close (using post-DD high by default; configurable to close-source).

Today's DD is never invalidated immediately because there are no post-DD sessions to evaluate the 5% gain against.

Counting Conventions

  • d5_count / d15_count / d25_count count active records with age_sessions <= N.
  • This means N+1 sessions are inspected (age 0..N inclusive). Reports therefore say "within N elapsed sessions" rather than "直近 N 取引日" to avoid ambiguity.

Risk Classification

RiskTrigger
NORMALd25 <= 2
CAUTIONd25 >= 3
HIGHd25 >= 5 OR d15 >= 3 OR d5 >= 2
SEVEREd25 >= 6 OR d15 >= 4 OR (market_below_21ema_or_50ma AND d25 >= 5)

When both QQQ and SPY are loaded, QQQ-weighted overall logic applies (TQQQ-aware): a single SEVERE escalates to SEVERE; QQQ HIGH escalates to overall HIGH; QQQ NORMAL + SPY HIGH still escalates to HIGH (broad-market spillover).

TQQQ Exposure Policy

RiskActionTarget ExposureTrailing Stop
NORMALHOLD_OR_FOLLOW_BASE_STRATEGY100%base
CAUTIONAVOID_NEW_ADDS75%min(base, 7%)
HIGHREDUCE_EXPOSURE50%min(base, 5%)
SEVERECLOSE_TQQQ_OR_HEDGE25%min(base, 3%)

QQQ uses a less aggressive policy (HIGH=75%, SEVERE=50%) since it lacks 3x leverage.

Workflow

  1. Load OHLCV for the configured symbols via FMP (get_historical_prices).
  2. Validate data quality; record skipped sessions in audit.
  3. Rebase via prepare_effective_history so effective_history[0] is the evaluation session.
  4. Detect raw Distribution Days; enrich with high_since, invalidation event, and status.
  5. Count d5 / d15 / d25 active records.
  6. Compute 21EMA and 50SMA filters; flag market_below_21ema_or_50ma (None if data insufficient).
  7. Classify each index, then combine using QQQ-weighted policy.
  8. Generate portfolio action for the configured instrument.
  9. Write JSON + Markdown reports to --output-dir with API keys redacted.

Outputs

Saved to reports/ (or --output-dir):

  • ibd_distribution_day_monitor_YYYY-MM-DD_HHMMSS.json
  • ibd_distribution_day_monitor_YYYY-MM-DD_HHMMSS.md

JSON is UTF-8 with ensure_ascii=False (Japanese explanations preserved). Sensitive keys (api_key, fmp_api_key, token, etc.) are redacted automatically.

Operating Principles

  • Do not override the IBD rule definitions unless config/default.yaml is changed deliberately.
  • Always explain which dates contributed to the active count.
  • Treat missing or unreliable volume data as a warning (audit_flag), not as a Distribution Day.
  • Do not place trades. The portfolio action is a risk-management suggestion, not an execution instruction.

CLI

python3 skills/ibd-distribution-day-monitor/scripts/ibd_monitor.py \
  --symbols QQQ,SPY \
  --lookback-days 80 \
  --instrument TQQQ \
  --current-exposure 100 \
  --base-trailing-stop 10 \
  --output-dir reports/

API Requirements

FMP API key required. Free tier (250 calls/day) is sufficient for daily QQQ + SPY runs.

Related Skills

  • ftd-detector: Bottom confirmation via Follow-Through Days (counterpart of this top-side signal).
  • market-top-detector: Composite 0-100 top probability score using O'Neil distribution + other components.
  • position-sizer: Convert risk-management recommendations into share counts.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Plan and assess backtests, which test trading rules against historical data. Examine sensitivity to rule changes, trading costs, and data bias, then use supplied performance metrics to generate a structured evaluation with reasons that block adoption.

中文简介正在准备中。当前显示英语简介。

  • Define a backtesting plan for rule-based trading
  • Test sensitivity to parameters and trading costs
  • Investigate suspiciously strong backtest results
tradermonty/claude-trading-skills32522026年10月24日 更新

Assess how broadly US stock market gains are shared across stocks using public CSV data and optional charts. Produce English reports covering market conditions, trading signals, scenarios, and positioning for different investment timeframes.

中文简介正在准备中。当前显示英语简介。

  • Checking how broadly US stocks are participating in a market rise
  • Reviewing medium- to long-term market positioning
  • Assessing short-term swing trading signals
tradermonty/claude-trading-skills32522026年10月24日 更新

Analyze chart images showing how broadly US stocks are participating in market trends. Assess medium-term conditions and short-term signals, then produce an English report with positioning guidance, alternative scenarios, and risks based on the visible data.

中文简介正在准备中。当前显示英语简介。

  • Assessing the market cycle from an S&P 500 breadth chart
  • Checking short-term signals in an uptrend stock ratio chart
  • Comparing medium-term and short-term market signals
tradermonty/claude-trading-skills32522026年10月24日 更新

Turn VCP screening results, which identify narrowing price swings, into breakout trade plans. Calculate entry, stop-loss, target prices, and position sizes using worst-case entries, while applying portfolio risk limits and producing Alpaca-compatible order templates.

中文简介正在准备中。当前显示英语简介。

  • Planning entries, stop-losses, and profit targets for VCP candidates
  • Calculating position sizes from account size and risk limits
  • Checking total portfolio risk and sector exposure
tradermonty/claude-trading-skills32522026年10月24日 更新

Screen US growth stocks with William O'Neil's CANSLIM method, which evaluates seven factors including earnings, price strength, trading volume, and market direction. Receive ranked candidates with composite scores, factor breakdowns, market warnings, and data quality notes.

中文简介正在准备中。当前显示英语简介。

  • Find US stocks with earnings growth and price momentum
  • Compare a chosen list of stocks using CANSLIM scores
  • Screen a sector-focused stock list
tradermonty/claude-trading-skills32522026年10月24日 更新

Combine three reports on crowded positions, failed reactions to news, and weekly price reversals into one trading setup status. Identify missing or invalid evidence and determine whether the setup is ready for planning.

中文简介正在准备中。当前显示英语简介。

  • Combine three contrarian strategy reports
  • Check which confirmations are still missing
  • Find stale or inconsistent input reports
tradermonty/claude-trading-skills32522026年10月24日 更新

查看 tradermonty 的全部技能

报告此技能的问题