Market Cycle Dashboard - toddbadams/strawberry GitHub Wiki

This document outlines key market cycle detection metrics organized by epic (signal type) with full user stories, data specs, scoring models, and Alpha Vantage endpoints.

EPIC 1: Cycle-Sensitive Macroeconomic Indicators

Goal: Detect expansions, contractions, and inflection points in the macro environment.

1.1 ISM Manufacturing PMI

As a quantitative analyst, I want to monitor the ISM PMI monthly So that I can detect manufacturing-led expansions or contractions early.

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column ISM_PMI
Output Column ISM_PMI_Score
Formula Raw value from ISM PMI
Scoring 40 β†’ 60 maps to 0–100, ascending
Alpha Vantage function=ISM_MANUFACTURING
Release Date 1st business day of each month

Indicative Data Load Process

flowchart TD
    %% Flow Nodes
    A[Start Data Acquisition]
    B{Is data stored locally?}
    C{Is data current<br>per config rules?}
    D[Skip API call]
    E[Fetch from Alpha Vantage API]
    G[Store Locally]
    H[Done]

    %% Flow Logic
    A --> B
    B -- No --> E
    B -- Yes --> C
    C -- No --> E
    C -- Yes --> D
    E --> G
    G --> H
    D --> H

    %% Styling for dark mode
    style A fill:#222,stroke:#0ff,stroke-width:2px,color:#0ff
    style B fill:#333,stroke:#ff0,stroke-width:1px,color:#ff0
    style C fill:#333,stroke:#ff0,stroke-width:1px,color:#ff0
    style D fill:#222,stroke:#0f0,stroke-width:1px,color:#0f0
    style E fill:#400,stroke:#f00,stroke-width:1px,color:#f00
    style G fill:#020,stroke:#0f0,stroke-width:1px,color:#0f0
    style H fill:#222,stroke:#0ff,stroke-width:2px,color:#0ff

1.2 GDP Growth (QoQ Annualized)

As a quantitative analyst, I want to track quarterly GDP growth So that I can detect macro turning points.

Acceptance Criteria:

Field Value
Frequency Quarterly
Input Column GDP_Growth
Output Column GDP_Growth_Score
Formula Raw GDP growth
Scoring –5 β†’ +5 maps to 0–100, ascending
Alpha Vantage function=REAL_GDP
Release Date End of month after quarter

1.3 Unemployment Rate

As a quantitative analyst, I want to monitor the unemployment rate So that I can measure labor market deterioration.

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column Unemployment_Rate
Output Column Unemployment_Score
Formula Raw unemployment rate
Scoring 10 β†’ 3 maps to 0–100, descending
Alpha Vantage function=UNEMPLOYMENT_RATE
Release Date 1st Friday of each month

1.4 Consumer Confidence Index

As a quantitative analyst, I want to track consumer confidence to understand sentiment-driven spending trends.

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column Consumer_Confidence
Output Column Consumer_Confidence_Score
Formula Raw index value
Scoring 50 β†’ 130 maps to 0–100, ascending
Alpha Vantage function=CONSUMER_SENTIMENT
Release Date End of month

1.5 Retail Sales (YoY)

As a quantitative analyst, I want to calculate YoY retail sales growth to gauge consumer demand trends.

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column Retail_Sales_YoY
Output Column Retail_Sales_Score
Formula YoY % change of retail sales
Scoring –5 β†’ +10 maps to 0–100, ascending
Alpha Vantage function=RETAIL_SALES
Release Date Mid-month

1.6 CPI Inflation (YoY)

As a quantitative analyst, I want to track YoY CPI inflation to identify mid-to-late cycle pressures.

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column CPI_YoY
Output Column CPI_Score
Formula YoY % change of CPI
Scoring 8 β†’ 1 maps to 0–100, descending
Alpha Vantage function=CPI
Release Date Mid-month

1.7 Yield Curve (10Y – 2Y Spread)

As a quantitative analyst, I want to compute and track the 10Y–2Y Treasury yield spread to detect potential recessions.

Acceptance Criteria:

Field Value
Frequency Weekly
Input Column Yield_Spread
Output Column Yield_Spread_Score
Formula 10Y_Yield - 2Y_Yield
Scoring –1 β†’ +2 maps to 0–100, ascending
Alpha Vantage function=TREASURY_YIELD
Release Date Daily (sample weekly)

EPIC 2: Market Internals

Goal: Monitor price and volume-based signals that often move before macro indicators.

2.1 Advance/Decline Line

As a quantitative analyst I want to** track the Advance/Decline (A/D) line So that I can assess market breadth and spot early warnings of internal weakness

Use NYSE or S&P 500 Advance/Decline data as a broad market signal.

Acceptance Criteria:

Field Value
Frequency Daily/Weekly
Input Column AD_Line
Output Column AD_Score
Formula #Advancers - #Decliners (cumulative)
Scoring Slope of A/D line, normalized to 0–100, ascending
Alpha Vantage ❌ Not directly available
Release Date Daily β€” NYSE/market data feed

Why Daily Is the Right Frequency

You’re building a market cycle detection engine, and the Advance/Decline Line is:

  • A breadth indicator, not a trading signal
  • Most meaningful at the daily or weekly level
  • Used to detect divergences and internal market strength/weakness over time

How to Build an A/D Line from Alpha Vantage

  1. **Choose the S&P 100 (manageable for API limits)
tickers = [AAPL,ABBV,ABT,ACN,ADBE,AMD,AMGN,AMT,AMZN,AVGO  
        AXP,BA,BAC,BIIB,BK,BKR,BMY,BRK.B,C,CAT  
        CHTR,CL,CMCSA,COF,COP,COST,CRM,CSCO,CVS,CVX  
        DD,DE,DHR,DIS,DOW,DUK,EMR,EXC,F,FDX  
        GD,GE,GM,GOOGL,GS,HD,HON,IBM,INTC,JNJ  
        JPM,KHC,KMI,KO,LIN,LMT,LRCX,LOW,MA,MCD  
        MDLZ,MDT,MET,META,MMM,MO,MRK,MS,MSFT,NEE  
        NFLX,NKE,NOW,NVDA,ORCL,PEP,PFE,PG,PM,PYPL  
        QCOM,RTX,SBUX,SO,SPG,T,TMUS,TMO,TSLA,TXN  
        UNH,UNP,UPS,V,VZ,WBA,WFC,WMT,XOM,ZTS]

  1. Query each ticker’s daily close

    https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=AAPL&apikey=YOUR_KEY

  2. Parse the most recent two close prices

  3. Classify each ticker as:

    • Advancer: close_today > close_yesterday
    • Decliner: close_today < close_yesterday
    • Unchanged: ignore or count as neutral

  4. Aggregate: net_advances = advancers - decliners

  5. Cumulatively sum over time to produce the A/D line

Sample Pseudocode


tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META']  # replace with S&P 100
ts = TimeSeries(key='YOUR_API_KEY', output_format='pandas')

ad_data = []

for ticker in tickers:
    data, _ = ts.get_daily_adjusted(symbol=ticker, outputsize='compact')
    data = data.sort_index()
    try:
        latest = data.iloc[-1]['5. adjusted close']
        previous = data.iloc[-2]['5. adjusted close']
        ad_data.append(1 if latest > previous else -1 if latest < previous else 0)
    except:
        continue
    time.sleep(12)  # throttle to avoid API limit

net_ad = sum(ad_data)

2.2 % of Stocks Above 200-Day MA

As a quantitative analyst

  • I want to monitor what percentage of stocks are above their 200-day moving averages
  • So that I can detect broad-based strength or weakness

Acceptance Criteria:

Field Value
Frequency Daily/Weekly
Input Column Pct_Above_200DMA
Output Column Pct_Above_200DMA_Score
Formula # stocks above 200DMA / total stocks
Scoring min: 10%, max: 90%, ascending
Alpha Vantage ❌ Must calculate using TIME_SERIES_DAILY_ADJUSTED per stock
Release Date Daily

How to Get the Data

Component Recommendation
Data Source Calculate using Alpha Vantage’s TIME_SERIES_DAILY_ADJUSTED for a list of tickers
Free Option Use yfinance for a limited set (e.g. S&P 100)
Premium Use full index data from Polygon, Tiingo, or Barchart
Processing For each ticker: 200DMA = close.rolling(200).mean()
Output % = count(stocks_above_200dma) / total_stocks
Frequency Daily or weekly sampling

Sample Logic

above_200dma = [ticker for ticker in tickers if latest_price[ticker] > dma_200[ticker]]
pct_above = len(above_200dma) / len(tickers) * 100

2.3 VIX (Volatility Index)

As a quantitative analyst

  • I want to track the VIX index
  • So that I can identify shifts in market fear or complacency

Acceptance Criteria:

Field Value
Frequency Daily
Input Column VIX
Output Column VIX_Score
Formula Raw VIX index
Scoring min: 10, max: 60, descending
Alpha Vantage ❌ Not available directly; use CBOE
Release Date Real-time (CBOE)

How to Get the Data

Component Recommendation
Data Source CBOE VIX
Free Option Yahoo Finance: ^VIX via yfinance
Premium CBOE subscription (real-time) or Barchart.com
Alpha Vantage ❌ Not available
Storage Daily close values of VIX

Sample Logic

import yfinance as yf
vix_data = yf.download("^VIX", start="2020-01-01", end="today")
vix_score = scale_to_score(vix_data["Close"].iloc[-1], min_val=10, max_val=60, descending=True)

2.4 Credit Spreads (High Yield – Treasury)

As a quantitative analyst

  • I want to track high-yield vs Treasury yield spreads
  • So that I can detect stress or tightening in credit markets

Acceptance Criteria:

Field Value
Frequency Weekly
Input Column Credit_Spread
Output Column Credit_Spread_Score
Formula HY_Corp_Yield – 10Y_Treasury_Yield
Scoring min: 0.5%, max: 6%, descending
Alpha Vantage ❌ Requires external source (FRED / ICE)
Release Date Weekly (usually Fridays)

EPIC 3: Valuation Indicators

Goal: Identify regimes where the market may be overextended or undervalued over longer horizons.

3.1 Shiller CAPE Ratio

As a quantitative analyst

  • I want to evaluate long-term valuations using the CAPE ratio
  • So that I can assess long-run expected returns

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column Shiller_CAPE
Output Column CAPE_Score
Formula Raw value
Scoring min: 10, max: 40, descending
Alpha Vantage ❌ Not supported β€” use multpl.com or FRED
Release Date Monthly (delayed by a few weeks)

3.2 Forward P/E of S&P 500

As a quantitative analyst I want to** monitor forward P/E multiples So that I can spot valuation compression or expansion trends

Acceptance Criteria:

Field Value
Frequency Weekly/Monthly
Input Column Forward_PE
Output Column Forward_PE_Score
Formula Raw P/E
Scoring min: 10, max: 25, descending
Alpha Vantage ❌ Not available β€” use FactSet/S&P
Release Date Weekly (FactSet); Monthly (S&P)

3.3 Market Cap to GDP ("Buffett Indicator")

As a quantitative analyst I want to** compare total market cap to GDP So that I can assess extreme over- or undervaluation levels

Acceptance Criteria:

Field Value
Frequency Quarterly
Input Column MarketCap_GDP
Output Column Buffett_Score
Formula Total Market Cap / Nominal GDP
Scoring min: 0.7, max: 2.0, descending
Alpha Vantage ❌ Requires total market cap + GDP (FRED + YCharts)
Release Date Quarterly

EPIC 4: Sentiment & Positioning

Goal: Spot emotional extremes and crowd positioning that signal reversals.

4.1 AAII Sentiment Survey

As a quantitative analyst I want to** track the AAII bull-bear spread So that I can fade extreme sentiment

Acceptance Criteria:

Field Value
Frequency Weekly
Input Column AAII_Spread
Output Column AAII_Score
Formula Bull % – Bear %
Scoring min: –40, max: +40, mean-reverting
Alpha Vantage ❌ Must scrape from AAII site
Release Date Weekly (Thursdays)

4.2 Put/Call Ratio

As a quantitative analyst I want to** track the CBOE equity put/call ratio So that I can spot fear vs greed sentiment

Acceptance Criteria:

Field Value
Frequency Daily
Input Column PutCall_Ratio
Output Column PutCall_Score
Formula Raw ratio
Scoring min: 0.4, max: 1.2, mean-reverting
Alpha Vantage ❌ Use CBOE or FRED data
Release Date Daily

4.3 Fund Manager Survey (BofA Global Fund Manager Survey)

As a quantitative analyst I want to track institutional positioning and risk appetite via the Fund Manager Survey So that I can identify excessive risk-on or risk-off sentiment among professional investors

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column Fund_Manager_Risk_Exposure
Output Column Fund_Manager_Score
Formula % of managers overweight equities or cash
Scoring 0%–100%, mean-reverting
Alpha Vantage ❌ Not available β€” must pull from BofA PDF or secondary data feed
Release Date Mid-month (typically around the 3rd week)

EPIC 5: Leading Indicator Composites

Goal: Track indexes that combine multiple forward-looking signals.

5.1 Conference Board LEI

As a quantitative analyst I want to** track the U.S. Leading Economic Index So that I can anticipate economic slowdowns

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column LEI
Output Column LEI_Score
Formula % change MoM or YoY
Scoring Positive = high score, negative = low, ascending
Alpha Vantage ❌ Must pull from Conference Board
Release Date Around the 20th of each month

5.2 OECD Composite Leading Indicator**

As a quantitative analyst I want to* track global composite leading indicators So that I can adjust exposure based on international cycle changes

Acceptance Criteria:

Field Value
Frequency Monthly
Input Column OECD_CLI
Output Column OECD_CLI_Score
Formula % deviation from long-term trend
Scoring –2% to +2% = 0–100, ascending
Alpha Vantage ❌ Use OECD API or FRED
Release Date Monthly (1–2 month lag)

Absolutely β€” here’s a well-structured Epic that covers the UI/UX work for integrating your metrics into a Market Cycle Dashboard, with regime classification and visual cues for user interpretation.

EPIC 6: Market Cycle Dashboard UI

Goal: Present a unified, visual dashboard that summarizes all cycle metrics and clearly communicates the current market regime.

As a quantitative analyst, I want to view a Market Cycle Dashboard that aggregates all scored indicators So that I can quickly assess where we are in the macro cycle and adjust strategy accordingly.

Acceptance Criteria

  • βœ… Display a summary view of current scores across all metrics grouped by signal type

  • βœ… Visually indicate the current market regime (Early, Mid, Late, Recession) using:

    • Color-coded regime cards
    • Historical regime transitions (timeline/chart)
    • Confidence score or probability (if model-driven)

  • βœ… Provide tooltip explanations for each regime signal (e.g., β€œPMI rising = early cycle”)

  • βœ… Offer a drill-down panel for each metric with:

    • Current score
    • Trend chart (3–12 months)
    • Last release date and source

  • βœ… Include a regime classification engine output (deterministic or ML-based) with inputs:

    • Aggregated composite score
    • Top contributing indicators
    • Threshold logic per regime

Regime Definitions (for logic layer & UI labels)

| Regime | Leading Signals | | | -- | | Early Cycle | Rising PMI, improving earnings, steepening yield curve | | Mid Cycle | Strong GDP & employment, valuations stretched | | Late Cycle | Rising inflation, flattening/inverted yield curve | | Recession | Negative GDP, rising unemployment, volatility spike, rate cuts |

UI Components to Build

Component Purpose
RegimeHeaderCard Show current regime + key signals
ScoreHeatmapGrid Display normalized scores per metric
RegimeTimeline Track historical regime classification changes
MetricDrilldownPanel View source, formula, chart, and trend for each
CycleExplanationModal Explain regime logic & transition signals

Output Examples

  • Current Market Cycle: Late Cycle
  • Confidence Score: 78/100
  • Top Contributors: Yield curve inverted, CPI rising, VIX spiking
  • Last Update: July 2025, all data current

Let me know if you’d like a wireframe layout, React component structure, or integration plan into an existing analytics portal.

Market Cycle Dashboard

+----------------------------------------------------------+
|                    MARKET CYCLE DASHBOARD                |
+----------------------------------------------------------+
|   [ LATE CYCLE ]                                         |
|   Confidence Score: 78/100                               |
|   Yield curve inverted, CPI rising, VIX spiking          |
|   Last Update: Jul 2025                                  |
+----------------------------------------------------------+

| EARLY CYCLE | MID CYCLE | LATE CYCLE | RECESSION |
+--------------------------------------------------+
|           Summary View of Cycle Regimes          |
+--------------------------------------------------+

+------------------------+-------------------------+
|   Score Heatmap Grid   |      Timeline Chart     |
|  +-----+-----+------+  |  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  |
|  |  EC | MC  |  LC  |  |  β”‚                   β”‚  |
|  |-----|-----|------|  |  β”‚   Regime Trend    β”‚  |
|  |  REC| INF | VIX  |  |  β”‚   Over Time β†’     β”‚  |
|  +-----+-----+------+  |  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜  |
+------------------------+-------------------------+

+------------------------+-------------------------+
|     Cycle Explanation  |   Metric Drilldown Panel|
|  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” |  +--------------------+ |
|  β”‚ Early = PMI↑      β”‚ |  |  CPI YoY: 6.2%     | |
|  β”‚ Recession = GDP↓  β”‚ |  |  Score: 22/100     | |
|  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ |  |  Trend: β†˜ 3 mo      | |
|       [Learn More]     |  +--------------------+ |
+------------------------+-------------------------+

Notes

  • replace "EC", "MC", "LC", "REC" in the heatmap grid with actual metric abbreviations.
  • insert actual values into metric panels programmatically.
flowchart TD
    A[MARKET CYCLE DASHBOARD]

    subgraph Current Regime
        B[LATE CYCLE]
        Bdesc[Confidence: 78/100<br>Yield curve inverted, CPI rising, VIX spiking<br>Last Update: Jul 2025]
    end

    subgraph Regime Summary View
        R1[EARLY CYCLE]
        R2[MID CYCLE]
        R3[LATE CYCLE]
        R4[RECESSION]
    end

    subgraph Score View
        H[Score Heatmap Grid]
        T[Timeline Chart]
    end

    subgraph Insights
        X[Cycle Explanation]
        Y[Metric Drilldown Panel]
    end

    A --> B
    B --> Bdesc
    Bdesc --> R1
    R1 --> R2 --> R3 --> R4
    R4 --> H
    H --> T
    T --> X
    X --> Y