机构资金流向追踪器：分析13F文件追踪聪明钱，提前发现投资机会

institutional-flow-tracker by tradermonty/claude-trading-skills

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394 GitHub Stars

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安装命令

npx skills add https://github.com/tradermonty/claude-trading-skills --skill institutional-flow-tracker

自动化数据分析金融科技

🇨🇳中文介绍

机构资金流向追踪器

概述

本技能通过分析 13F SEC 文件来追踪机构投资者的活动，识别流入和流出股票的“聪明钱”。通过分析机构持股的季度变化，您可以在主要价格变动之前发现老练投资者正在积累的股票，或在机构减持时识别潜在风险。

核心洞察： 机构投资者（对冲基金、养老基金、共同基金）管理着数万亿美元的资金并进行广泛研究。他们的集体买卖模式通常领先于显著的价格变动 1-3 个季度。

前提条件

FMP API 密钥： 设置 FMP_API_KEY 环境变量或向脚本传递 --api-key
Python 3.8+： 运行分析脚本所需
依赖项： pip install requests（脚本会优雅地处理缺失的依赖项）

何时使用此技能

在以下情况使用此技能：

验证投资想法（检查聪明钱是否同意您的论点）
发现新机会（寻找机构正在积累的股票）
风险评估（识别机构正在退出的股票）
投资组合监控（追踪您持仓的机构支持情况）
跟踪特定投资者（追踪沃伦·巴菲特、凯茜·伍德等）
板块轮动分析（识别机构资金正在轮动的方向）

不要在以下情况使用：

寻求实时盘中信号（13F 数据有 45 天的报告延迟）
分析微型市值股票（市值 < 1 亿美元，机构兴趣有限）
寻找短期交易信号（时间范围 < 3 个月）

数据源与要求

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必需：FMP API 密钥

此技能使用 Financial Modeling Prep (FMP) API 来访问 13F 文件数据：

# 设置环境变量（推荐）
export FMP_API_KEY=your_key_here

# 或者在运行脚本时提供
python3 scripts/track_institutional_flow.py --api-key YOUR_KEY

API 层级要求：

免费层级： 250 次请求/天（足以分析 20-30 只股票的季度数据）
付费层级： 更广泛的筛选有更高的限制

13F 文件提交时间表：

季度结束后 45 天内提交
Q1 (1月-3月)：5月中旬前提交
Q2 (4月-6月)：8月中旬前提交
Q3 (7月-9月)：11月中旬前提交
Q4 (10月-12月)：2月中旬前提交

步骤 1：识别机构持股发生显著变化的股票

执行主要筛选脚本，以发现具有显著机构活动的股票：

快速扫描（按机构变化排名前 50 的股票）：

python3 scripts/track_institutional_flow.py \
  --top 50 \
  --min-change-percent 10

聚焦特定板块的扫描：

python3 scripts/track_institutional_flow.py \
  --sector Technology \
  --min-institutions 20

自定义筛选：

python3 scripts/track_institutional_flow.py \
  --min-market-cap 2000000000 \
  --min-change-percent 15 \
  --top 100 \
  --output institutional_flow_results.json

股票代码和公司名称
当前机构持股比例（占流通股比例）
季度环比持股数量变化
持股机构数量
机构数量变化（新买家 vs 卖家）
主要机构持有人

步骤 2：对特定股票进行深入分析

对特定股票的机构持股进行详细分析：

python3 scripts/analyze_single_stock.py AAPL

历史机构持股趋势（8 个季度）
所有机构持有人及其仓位变化列表
集中度分析（前 10 大持有人占机构总持股的比例）
新开仓位 vs 增持 vs 减持仓位
包含可靠性评级的数据质量评估

需要评估的关键指标：

持股比例： 机构持股比例越高（>70%）= 更稳定但上涨空间有限
持股趋势： 持股比例上升 = 看涨，下降 = 看跌
集中度： 高集中度（前 10 大 > 50%）= 如果他们卖出则有风险
持有人质量： 是否存在优质长期投资者（伯克希尔、富达）vs 动量基金

步骤 3：追踪特定机构投资者

注意： track_institution_portfolio.py 尚未实现。FMP API 按股票（而非按机构）组织机构持有人数据，仅通过此 API 重建完整投资组合是不切实际的。

替代方法 — 使用 analyze_single_stock.py 检查特定机构是否持有某只股票：

# 分析一只股票，并在输出中查找特定机构
python3 institutional-flow-tracker/scripts/analyze_single_stock.py AAPL
# 然后在报告中搜索前 20 大持有人表格中的 "Berkshire" 或 "ARK"

要进行完整的机构层面投资组合追踪，请使用以下外部资源：

WhaleWisdom： https://whalewisdom.com（提供免费层级，13F 投资组合查看器）
SEC EDGAR： https://www.sec.gov/cgi-bin/browse-edgar（官方 13F 文件）
DataRoma： https://www.dataroma.com（超级投资者投资组合追踪器）

步骤 4：解读与行动

阅读参考资料以获取解读指导：

references/13f_filings_guide.md - 了解 13F 数据及其局限性
references/institutional_investor_types.md - 不同类型的投资者及其策略
references/interpretation_framework.md - 如何解读机构资金流向信号

信号强度框架：

强烈看涨（考虑买入）：

机构持股季度环比增长 >15%
机构数量增长 >10%
优质长期投资者正在增持
当前持股比例低（<40%），有增长空间
多个季度持续积累

机构持股季度环比增长 5-15%
新买家和卖家混合，净值为正
当前持股比例 40-70%

持股比例变化极小（<5%）
买卖双方数量相近
稳定的机构基础

机构持股季度环比下降 5-15%
卖家多于买家
高持股比例（>80%）限制了新买家

强烈看跌（考虑卖出/避免）：

机构持股季度环比下降 >15%
机构数量下降 >10%
优质投资者正在退出仓位
多个季度持续派发
集中度风险（大股东大量卖出）

步骤 5：投资组合应用

对于新仓位：

对您的股票想法进行机构分析
寻找确认信号（机构也在积累）
如果出现强烈看跌信号，重新考虑或减少仓位规模
如果出现强烈看涨信号，增强对论点的信心

对于现有持仓：

在 13F 文件提交截止日期后进行季度审查
监控派发情况（早期预警系统）
如果机构正在退出，重新评估您的论点
如果机构普遍卖出，考虑减仓

筛选工作流程集成：

使用价值股息筛选器或其他筛选器寻找候选股票
对顶级候选股票运行机构资金流向追踪器
优先考虑机构正在积累的股票
避免机构正在派发的股票

所有分析都会生成结构化的 Markdown 报告，并保存到仓库根目录：

文件名约定： institutional_flow_analysis_<股票代码/主题>_<日期>.md

执行摘要（关键发现）
机构持股趋势（当前 vs 历史）
主要持有人及变化
新买家 vs 卖家
集中度分析
解读与建议
数据源与时间戳

数据可靠性评级

所有分析现在都包含基于数据质量的可靠性评级：

A 级： 覆盖率 < 3 倍，匹配率 >= 50%，真实持有人比例 >= 70%。可用于投资决策。
B 级： 真实持有人比例 >= 30%。仅供参考 - 谨慎使用。
C 级： 真实持有人比例 < 30%。不可靠 - 已从筛选结果中排除。

筛选脚本 (track_institutional_flow.py) 会自动排除 C 级股票。单只股票分析 (analyze_single_stock.py) 会显示评级并给出相应警告。

为什么这很重要： FMP 每个季度返回的持有人数量不同。一只股票可能在 Q4 显示有 5,415 个持有人，但在 Q3 只有 201 个。如果不进行过滤，汇总指标会产生误导性的百分比变化（例如，+400%）。数据质量模块会过滤到“真实”持有人（两个季度均存在），以生成可靠的指标。

局限性与注意事项

13F 文件有 45 天的报告延迟
自文件提交日期以来，仓位可能已发生变化
用作确认指标，而非领先信号

仅要求管理资产 > 1 亿美元的机构提交文件
不包括个人投资者和较小规模的基金
国际机构可能不提交 13F 文件

仅报告多头股票仓位（不包括空头、期权、债券）
持仓数据为季度末快照
某些仓位可能保密（延迟报告）

相关性 ≠ 因果关系（即使机构买入，股票也可能下跌）
考虑整体市场环境和基本面
结合技术分析和其他技能

内幕人士 + 机构组合：

寻找内幕人士和机构都在买入的股票
当两者一致时，信号尤其有力

板块轮动检测：

追踪按板块汇总的机构资金流向
在价格变动出现之前识别早期轮动趋势

寻找机构正在卖出的优质股票（潜在价值）
需要强大的基本面信念

聪明钱验证：

在建立主要仓位之前，检查聪明钱是否同意
增强信心或发现被忽视的风险

references/ 文件夹包含详细指南：

13f_filings_guide.md - 关于 13F SEC 文件的综合指南，包括其内容、报告要求和数据质量考量
institutional_investor_types.md - 不同类型的机构投资者（对冲基金、共同基金、养老基金等），其典型策略以及如何解读他们的动向
interpretation_framework.md - 解读机构持股变化的详细框架、信号质量评估以及与其他分析的整合

track_institutional_flow.py

用于寻找机构持股发生显著变化的股票的主要筛选脚本。

--api-key：FMP API 密钥（或设置 FMP_API_KEY 环境变量）

--top N：按机构变化返回前 N 只股票（默认：50）
--min-change-percent X：机构持股的最小百分比变化（默认：10）
--min-market-cap X：最小市值（美元）（默认：10亿）
--sector NAME：按特定板块筛选
--min-institutions N：机构持有人的最小数量（默认：10）
--limit N：从筛选器中获取的股票数量（默认：100）。较低的值可以节省 API 调用。
--output FILE：输出 JSON 文件路径
--output-dir DIR：报告的输出目录（默认：reports/）
--sort-by FIELD：按 'ownership_change' 或 'institution_count_change' 排序

analyze_single_stock.py

对特定股票的机构持股进行深入分析。

股票代码（位置参数）
--api-key：FMP API 密钥（或设置 FMP_API_KEY 环境变量）

--quarters N：要分析的季度数（默认：8，即 2 年）
--output FILE：输出 Markdown 报告路径
--output-dir DIR：报告的输出目录（默认：reports/）
--compare-to TICKER：将机构持股与另一只股票进行比较（未来功能）

track_institution_portfolio.py

状态：尚未实现

此脚本是一个占位符。它会打印替代资源（WhaleWisdom、SEC EDGAR、DataRoma）并以错误代码 1 退出。FMP API 按股票（而非按机构）组织机构持有人数据，使得完全重建投资组合不切实际。

要进行机构特定的投资组合追踪，请使用：

WhaleWisdom：https://whalewisdom.com（提供免费层级）
SEC EDGAR：https://www.sec.gov/cgi-bin/browse-edgar
DataRoma：https://www.dataroma.com

数据质量模块 (data_quality.py)

由 track_institutional_flow.py 和 analyze_single_stock.py 共享的实用工具模块：

classify_holder()： 将持有人分类为真实/新完整/已退出/未知
calculate_filtered_metrics()： 仅使用真实持有人计算指标
reliability_grade()： 根据数据质量分配 A/B/C 等级
is_tradable_stock()： 过滤掉 ETF、基金和非活跃股票
deduplicate_share_classes()： 移除 BRK-A/B、GOOG/GOOGL 等重复股票类别

与其他技能的集成

价值股息筛选器 + 机构资金流向：

1. 运行价值股息筛选器以寻找候选股票
2. 对每个候选股票检查机构资金流向
3. 优先考虑机构持股上升的股票

美股分析 + 机构资金流向：

1. 运行全面的基本面分析
2. 用机构持股趋势进行验证
3. 如果机构正在卖出，调查原因

投资组合管理器 + 机构资金流向：

1. 通过 Alpaca 获取当前投资组合
2. 对每个持仓进行机构分析
3. 标记机构支持恶化的仓位
4. 考虑从派发中重新平衡

技术分析师 + 机构资金流向：

1. 识别技术形态（例如，突破）
2. 检查机构买入是否确认
3. 如果两者一致，则信心更高

季度审查： 为 13F 文件提交截止日期设置日历提醒
多季度趋势： 寻找持续趋势（3 个季度以上），而非一次性变化
质量优于数量： 伯克希尔增持 > 100 个小基金增持
上下文很重要： 在下跌股票中持股比例上升，可能是价值投资者在接飞刀
结合信号： 切勿孤立使用机构资金流向
更新数据： 每个季度新 13F 文件提交后重新运行分析

FMP API 文档：https://financialmodelingprep.com/developer/docs
SEC 13F 文件数据库：https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&type=13F
机构投资者数据库：https://whalewisdom.com（提供免费层级）

注意： 此技能专为长期投资者（3-12 个月时间范围）设计。对于短期交易，请结合技术分析和其他动量指标。

🇺🇸English

Institutional Flow Tracker

Overview

This skill tracks institutional investor activity through 13F SEC filings to identify "smart money" flows into and out of stocks. By analyzing quarterly changes in institutional ownership, you can discover stocks that sophisticated investors are accumulating before major price moves, or identify potential risks when institutions are reducing positions.

Key Insight: Institutional investors (hedge funds, pension funds, mutual funds) manage trillions of dollars and conduct extensive research. Their collective buying/selling patterns often precede significant price movements by 1-3 quarters.

Prerequisites

FMP API Key: Set FMP_API_KEY environment variable or pass --api-key to scripts
Python 3.8+: Required for running analysis scripts
Dependencies: pip install requests (scripts handle missing dependencies gracefully)

When to Use This Skill

Use this skill when:

Validating investment ideas (checking if smart money agrees with your thesis)
Discovering new opportunities (finding stocks institutions are accumulating)
Risk assessment (identifying stocks institutions are exiting)
Portfolio monitoring (tracking institutional support for your holdings)
Following specific investors (tracking Warren Buffett, Cathie Wood, etc.)
Sector rotation analysis (identifying where institutions are rotating capital)

Do NOT use when:

Seeking real-time intraday signals (13F data has 45-day reporting lag)
Analyzing micro-cap stocks (<$100M market cap with limited institutional interest)
Looking for short-term trading signals (<3 months horizon)

Data Sources & Requirements

Required: FMP API Key

This skill uses Financial Modeling Prep (FMP) API to access 13F filing data:

Setup:

# Set environment variable (preferred)
export FMP_API_KEY=your_key_here

# Or provide when running scripts
python3 scripts/track_institutional_flow.py --api-key YOUR_KEY

API Tier Requirements:

Free Tier: 250 requests/day (sufficient for analyzing 20-30 stocks quarterly)
Paid Tiers: Higher limits for extensive screening

13F Filing Schedule:

Filed quarterly within 45 days after quarter end
Q1 (Jan-Mar): Filed by mid-May
Q2 (Apr-Jun): Filed by mid-August
Q3 (Jul-Sep): Filed by mid-November
Q4 (Oct-Dec): Filed by mid-February

Analysis Workflow

Step 1: Identify Stocks with Significant Institutional Changes

Execute the main screening script to find stocks with notable institutional activity:

Quick scan (top 50 stocks by institutional change):

python3 scripts/track_institutional_flow.py \
  --top 50 \
  --min-change-percent 10

Sector-focused scan:

python3 scripts/track_institutional_flow.py \
  --sector Technology \
  --min-institutions 20

Custom screening:

python3 scripts/track_institutional_flow.py \
  --min-market-cap 2000000000 \
  --min-change-percent 15 \
  --top 100 \
  --output institutional_flow_results.json

Output includes:

Stock ticker and company name
Current institutional ownership % (of shares outstanding)
Quarter-over-quarter change in shares held
Number of institutions holding
Change in number of institutions (new buyers vs sellers)
Top institutional holders

Step 2: Deep Dive on Specific Stocks

For detailed analysis of a specific stock's institutional ownership:

python3 scripts/analyze_single_stock.py AAPL

This generates:

Historical institutional ownership trend (8 quarters)
List of all institutional holders with position changes
Concentration analysis (top 10 holders' % of total institutional ownership)
New positions vs increased vs decreased positions
Data quality assessment with reliability grade

Key metrics to evaluate:

Ownership %: Higher institutional ownership (>70%) = more stability but limited upside
Ownership Trend: Rising ownership = bullish, falling = bearish
Concentration: High concentration (top 10 > 50%) = risk if they sell
Quality of Holders: Presence of quality long-term investors (Berkshire, Fidelity) vs momentum funds

Step 3: Track Specific Institutional Investors

Note: track_institution_portfolio.py is not yet implemented. FMP API organizes institutional holder data by stock (not by institution), making full portfolio reconstruction impractical via this API alone.

Alternative approach — useanalyze_single_stock.py to check if a specific institution holds a stock:

# Analyze a stock and look for a specific institution in the output
python3 institutional-flow-tracker/scripts/analyze_single_stock.py AAPL
# Then search the report for "Berkshire" or "ARK" in the Top 20 holders table

For full institution-level portfolio tracking, use these external resources:

WhaleWisdom: https://whalewisdom.com (free tier available, 13F portfolio viewer)
SEC EDGAR: https://www.sec.gov/cgi-bin/browse-edgar (official 13F filings)
DataRoma: https://www.dataroma.com (superinvestor portfolio tracker)

Step 4: Interpretation and Action

Read the references for interpretation guidance:

references/13f_filings_guide.md - Understanding 13F data and limitations
references/institutional_investor_types.md - Different investor types and their strategies
references/interpretation_framework.md - How to interpret institutional flow signals

Signal Strength Framework:

Strong Bullish (Consider buying):

Institutional ownership increasing >15% QoQ
Number of institutions increasing >10%
Quality long-term investors adding positions
Low current ownership (<40%) with room to grow
Accumulation happening across multiple quarters

Moderate Bullish:

Institutional ownership increasing 5-15% QoQ
Mix of new buyers and sellers, net positive
Current ownership 40-70%

Neutral:

Minimal change in ownership (<5%)
Similar number of buyers and sellers
Stable institutional base

Moderate Bearish:

Institutional ownership decreasing 5-15% QoQ
More sellers than buyers
High ownership (>80%) limiting new buyers

Strong Bearish (Consider selling/avoiding):

Institutional ownership decreasing >15% QoQ
Number of institutions decreasing >10%
Quality investors exiting positions
Distribution happening across multiple quarters
Concentration risk (top holder selling large position)

Step 5: Portfolio Application

For new positions:

Run institutional analysis on your stock idea
Look for confirmation (institutions also accumulating)
If strong bearish signals, reconsider or reduce position size
If strong bullish signals, gain confidence in thesis

For existing holdings:

Quarterly review after 13F filing deadlines
Monitor for distribution (early warning system)
If institutions are exiting, re-evaluate your thesis
Consider trimming if widespread institutional selling

Screening workflow integration:

Use Value Dividend Screener or other screeners to find candidates
Run Institutional Flow Tracker on top candidates
Prioritize stocks with institutional accumulation
Avoid stocks with institutional distribution

Output Format

All analysis generates structured markdown reports saved to repository root:

Filename convention: institutional_flow_analysis_<TICKER/THEME>_<DATE>.md

Report sections:

Executive Summary (key findings)
Institutional Ownership Trend (current vs historical)
Top Holders and Changes
New Buyers vs Sellers
Concentration Analysis
Interpretation and Recommendations
Data Sources and Timestamp

Data Reliability Grades

All analysis now includes a reliability grade based on data quality:

Grade A: Coverage ratio < 3x, match ratio >= 50%, genuine holder ratio >= 70%. Safe for investment decisions.
Grade B: Genuine holder ratio >= 30%. Reference only - use with caution.
Grade C: Genuine holder ratio < 30%. UNRELIABLE - excluded from screening results.

The screening script (track_institutional_flow.py) automatically excludes Grade C stocks. The single stock analysis (analyze_single_stock.py) displays the grade with appropriate warnings.

Why this matters: FMP returns different numbers of holders per quarter. A stock may show 5,415 holders in Q4 but only 201 in Q3. Without filtering, aggregate metrics produce misleading percent changes (e.g., +400%). The data quality module filters to "genuine" holders (present in both quarters) to produce reliable metrics.

Limitations and Caveats

Data Lag:

13F filings have 45-day reporting delay
Positions may have changed since filing date
Use as confirming indicator, not leading signal

Coverage:

Only institutions managing >$100M are required to file
Excludes individual investors and smaller funds
International institutions may not file 13F

Reporting Rules:

Only long equity positions reported (no shorts, options, bonds)
Holdings as of quarter-end snapshot
Some positions may be confidential (delayed reporting)

Interpretation:

Correlation ≠ causation (stocks can fall despite institutional buying)
Consider overall market environment and fundamentals
Combine with technical analysis and other skills

Advanced Use Cases

Insider + Institutional Combo:

Look for stocks where both insiders AND institutions are buying
Particularly powerful signal when aligned

Sector Rotation Detection:

Track aggregate institutional flows by sector
Identify early rotation trends before they appear in price

Contrarian Plays:

Find quality stocks institutions are selling (potential value)
Requires strong fundamental conviction

Smart Money Validation:

Before major position, check if smart money agrees
Gain confidence or find overlooked risks

References

The references/ folder contains detailed guides:

13f_filings_guide.md - Comprehensive guide to 13F SEC filings, what they include, reporting requirements, and data quality considerations
institutional_investor_types.md - Different types of institutional investors (hedge funds, mutual funds, pension funds, etc.), their typical strategies, and how to interpret their moves
interpretation_framework.md - Detailed framework for interpreting institutional ownership changes, signal quality assessment, and integration with other analysis

Script Parameters

track_institutional_flow.py

Main screening script for finding stocks with significant institutional changes.

Required:

--api-key: FMP API key (or set FMP_API_KEY environment variable)

Optional:

--top N: Return top N stocks by institutional change (default: 50)
--min-change-percent X: Minimum % change in institutional ownership (default: 10)
--min-market-cap X: Minimum market cap in dollars (default: 1B)
--sector NAME: Filter by specific sector
--min-institutions N: Minimum number of institutional holders (default: 10)
--limit N: Number of stocks to fetch from screener (default: 100). Lower values save API calls.
--output FILE: Output JSON file path
--output-dir DIR: Output directory for reports (default: reports/)
--sort-by FIELD: Sort by 'ownership_change' or 'institution_count_change'

analyze_single_stock.py

Deep dive analysis on a specific stock's institutional ownership.

Required:

Ticker symbol (positional argument)
--api-key: FMP API key (or set FMP_API_KEY environment variable)

Optional:

--quarters N: Number of quarters to analyze (default: 8, i.e., 2 years)
--output FILE: Output markdown report path
--output-dir DIR: Output directory for reports (default: reports/)
--compare-to TICKER: Compare institutional ownership to another stock (future feature)

track_institution_portfolio.py

Status: NOT YET IMPLEMENTED

This script is a placeholder. It prints alternative resources (WhaleWisdom, SEC EDGAR, DataRoma) and exits with error code 1. FMP API organizes institutional holder data by stock (not by institution), making full portfolio reconstruction impractical.

For institution-specific portfolio tracking, use:

WhaleWisdom: https://whalewisdom.com (free tier available)
SEC EDGAR: https://www.sec.gov/cgi-bin/browse-edgar
DataRoma: https://www.dataroma.com

Data Quality Module (data_quality.py)

Shared utility module used by both track_institutional_flow.py and analyze_single_stock.py:

classify_holder(): Classifies holders as genuine/new_full/exited/unknown
calculate_filtered_metrics(): Computes metrics using genuine holders only
reliability_grade(): Assigns A/B/C grade based on data quality
is_tradable_stock(): Filters out ETFs, funds, and inactive stocks
deduplicate_share_classes(): Removes BRK-A/B, GOOG/GOOGL duplicates

Integration with Other Skills

Value Dividend Screener + Institutional Flow:

1. Run Value Dividend Screener to find candidates
2. For each candidate, check institutional flow
3. Prioritize stocks with rising institutional ownership

US Stock Analysis + Institutional Flow:

1. Run comprehensive fundamental analysis
2. Validate with institutional ownership trends
3. If institutions are selling, investigate why

Portfolio Manager + Institutional Flow:

1. Fetch current portfolio via Alpaca
2. Run institutional analysis on each holding
3. Flag positions with deteriorating institutional support
4. Consider rebalancing away from distribution

Technical Analyst + Institutional Flow:

1. Identify technical setup (e.g., breakout)
2. Check if institutional buying confirms
3. Higher conviction if both align

Best Practices

Quarterly Reviews: Set calendar reminders for 13F filing deadlines
Multi-Quarter Trends: Look for sustained trends (3+ quarters), not one-time changes
Quality Over Quantity: Berkshire adding > 100 small funds adding
Context Matters: Rising ownership in a falling stock may be value investors catching a falling knife
Combine Signals: Never use institutional flow in isolation
Update Your Data: Re-run analysis each quarter as new 13Fs are filed

Support & Resources

FMP API Documentation: https://financialmodelingprep.com/developer/docs
SEC 13F Filings Database: https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&type=13F
Institutional Investor Database: https://whalewisdom.com (free tier available)

Note: This skill is designed for long-term investors (3-12 month horizon). For short-term trading, combine with technical analysis and other momentum indicators.

Weekly Installs

180

Repository

tradermonty/cla…g-skills

GitHub Stars

394

First Seen

Jan 26, 2026

Security Audits

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机构资金流向追踪器：分析13F文件追踪聪明钱，提前发现投资机会

🇨🇳中文介绍

机构资金流向追踪器

概述

前提条件

何时使用此技能

数据源与要求

相关 Skills

必需：FMP API 密钥

分析工作流程

步骤 1：识别机构持股发生显著变化的股票

步骤 2：对特定股票进行深入分析

步骤 3：追踪特定机构投资者

步骤 4：解读与行动

步骤 5：投资组合应用

输出格式

数据可靠性评级

局限性与注意事项

高级用例

参考资料

脚本参数

track_institutional_flow.py

analyze_single_stock.py

track_institution_portfolio.py

数据质量模块 (data_quality.py)

与其他技能的集成

最佳实践

支持与资源

🇺🇸English

Institutional Flow Tracker

Overview

Prerequisites

When to Use This Skill

Data Sources & Requirements

Required: FMP API Key

Analysis Workflow

Step 1: Identify Stocks with Significant Institutional Changes

Step 2: Deep Dive on Specific Stocks

Step 3: Track Specific Institutional Investors

Step 4: Interpretation and Action

Step 5: Portfolio Application

Output Format

Data Reliability Grades

Limitations and Caveats

Advanced Use Cases

References

Script Parameters

track_institutional_flow.py

analyze_single_stock.py

track_institution_portfolio.py

Data Quality Module (data_quality.py)

Integration with Other Skills

Best Practices

Support & Resources

最新 Skills