tools-only Uptrend Analyzer Methodology
Data Source: Monty's Uptrend Ratio Dashboard
Monty's Uptrend Ratio Dashboard tracks approximately 2,800 US stocks across 11 GICS sectors. For each stock, it determines whether the stock is in an "uptrend" based on the criteria below. The dashboard publishes daily CSV data on GitHub.
GitHub Repository: tradermonty/uptrend-dashboard
Live Dashboard: https://uptrend-dashboard.streamlit.app/
Uptrend Definition (Finviz Elite Screener)
A stock is classified as "uptrend" when it meets all of the following conditions:
| Condition | Description |
|---|---|
| Price > $10 | Penny stocks excluded |
| Avg Volume > 100K | Sufficient liquidity |
| Market Cap > $50M | Micro-cap and above |
| Price > SMA20 | Short-term uptrend |
| Price > SMA200 | Long-term uptrend |
| SMA50 > SMA200 | Golden cross (bullish structure) |
| 52W High/Low > 30% above Low | Recovering from bottom |
| 4-Week Performance: Up | Recent momentum positive |
The uptrend ratio = (stocks meeting all conditions) / (stocks meeting base filters: price, volume, market cap).
CSV Files
| File | Description | Update Frequency |
|---|---|---|
uptrend_ratio_timeseries.csv |
Daily ratios for "all" + 11 sectors | Daily |
sector_summary.csv |
Latest snapshot of all sectors | Daily |
Data availability:
- "all" (full market): Since 2023-08-11
- Sector-level data: Since 2024-07-21
Timeseries Columns
| Column | Type | Description |
|---|---|---|
| worksheet | string | "all" or sector slug (e.g., "sec_technology") |
| date | string | YYYY-MM-DD format |
| count | int | Number of stocks in uptrend |
| total | int | Total stocks tracked |
| ratio | float | count/total (0-1 scale, raw decimal) |
| ma_10 | float | 10-day simple moving average of ratio |
| slope | float | 1-day difference of ma_10 (ma_10.diff()) |
| trend | string | "up" (slope > 0) or "down" (slope <= 0) |
Sector Summary Columns
| Column | Type | Description |
|---|---|---|
| Sector | string | Display name (e.g., "Technology") |
| Ratio | float | Current uptrend ratio (0-1) |
| 10MA | float | 10-day MA of ratio |
| Trend | string | "Up" or "Down" |
| Slope | float | 1-day difference of MA |
| Status | string | "Overbought", "Oversold", or "Normal" |
Indicator Calculations (from source code)
All indicators are computed on-the-fly from raw count/total data:
ratio = count / total
ma_10 = ratio.rolling(10).mean() # 10-day simple MA
slope = ma_10.diff() # 1-day change of MA
trend = "up" if slope > 0 else "down"
Peak/Trough Detection: The dashboard uses scipy.signal.find_peaks with parameters distance=20, prominence=0.015 to identify local tops and bottoms in the 10MA series.
Official Dashboard Thresholds
These thresholds are defined in src/constants.py of the source repository:
| Threshold | Value | Meaning |
|---|---|---|
| Upper (Overbought) | 37% | Ratio above this = overbought conditions |
| Lower (Oversold) | 9.7% | Ratio below this = oversold / crisis |
| MA Period | 10 | Simple moving average window |
Status Determination
ratio > 0.37 -> "Overbought"
ratio < 0.097 -> "Oversold"
otherwise -> "Normal"
Practical Interpretation
| Ratio | Interpretation | Market Environment |
|---|---|---|
| 50%+ | Strong breadth | Broad bull market, most stocks participating |
| 37-50% | Overbought / Healthy | Above upper threshold, strong but extended |
| 25-37% | Normal / Recovering | Between thresholds, typical trading range |
| 9.7-25% | Weak | Below normal, breadth deteriorating |
| < 9.7% | Oversold / Crisis | Below lower threshold, extreme selling |
5-Component Scoring System
Component 1: Market Breadth (Overall) - Weight: 30%
Rationale: The overall uptrend ratio is the single most important measure of market health. A high ratio means broad participation; a low ratio means a narrow, fragile market.
Scoring Bands (aligned with dashboard thresholds):
| Ratio | Score Range | Signal |
|---|---|---|
| >= 50% | 90-100 | Strong Bull |
| 37-50% | 70-89 | Bullish (above overbought threshold) |
| 25-37% | 40-69 | Neutral/Recovering |
| 9.7-25% | 10-39 | Weak (between thresholds) |
| < 9.7% | 0-9 | Crisis (below oversold threshold) |
Trend Adjustment: +5 when trend="up" and slope>0, -5 when trend="down" and slope<0.
Component 2: Sector Participation - Weight: 25%
Rationale: A healthy market has most sectors participating. When only 2-3 sectors lead, the market is fragile and vulnerable to sector rotation shocks.
Sub-scores:
- Uptrend Count (60%): Number of sectors in uptrend mapped to 0-100
- Spread (40%): Max-min ratio spread. Narrow spread = uniform participation (good). Wide spread = selective market (risky).
Overbought/Oversold classification uses dashboard thresholds: >37% = Overbought, <9.7% = Oversold.
Component 3: Sector Rotation - Weight: 15%
Rationale: In a healthy bull market, cyclical sectors (Technology, Consumer Cyclical) lead defensive sectors (Utilities, Consumer Defensive). When defensives lead, it signals risk-off behavior.
Sector Classification:
| Group | Sectors |
|---|---|
| Cyclical | Technology, Consumer Cyclical, Communication Services, Financial, Industrials |
| Defensive | Utilities, Consumer Defensive, Healthcare, Real Estate |
| Commodity | Energy, Basic Materials |
Scoring: Based on cyclical_avg - defensive_avg difference.
- Cyclical lead > +15pp = Strong risk-on (90-100)
- Balanced within +/-5pp = Neutral (45-69)
- Defensive lead > +15pp = Strong risk-off (0-19)
Commodity Adjustment: When commodity sectors outperform both cyclical and defensive groups, it may signal late-cycle dynamics. A penalty of -5 to -10 is applied.
Component 4: Momentum - Weight: 20%
Rationale: The direction and rate of change in breadth matters as much as the level. Improving breadth (positive slope, accelerating) suggests the environment is getting better; deteriorating breadth suggests caution.
Sub-scores:
- Slope Score (50%): Current slope mapped to 0-100 (typical range: -0.02 to +0.02)
- Acceleration (30%): Recent 5-point slope average vs prior 5-point average
- Sector Slope Breadth (20%): Count of sectors with positive slope
Component 5: Historical Context - Weight: 10%
Rationale: Knowing where the current ratio falls in historical distribution provides perspective. A ratio that seems "low" might be historically average, or vice versa.
Scoring: Percentile rank of current ratio in the full historical distribution (Aug 2023 to present).
Note: The "all" dataset starts from 2023-08-11 (650+ data points), while sector data starts from 2024-07-21 (370+ data points each).
Scoring Zones and Exposure Guidance
| Score | Zone | Exposure | Description |
|---|---|---|---|
| 80-100 | Strong Bull | Full (100%) | Broad participation, strong momentum. Ideal for aggressive positioning. |
| 60-79 | Bull | Normal (80-100%) | Healthy breadth. Standard position management. |
| 40-59 | Neutral | Reduced (60-80%) | Mixed signals. Participate selectively. |
| 20-39 | Cautious | Defensive (30-60%) | Weak breadth. Prioritize capital preservation. |
| 0-19 | Bear | Preservation (0-30%) | Severe deterioration. Maximum defense. |
Weight Rationale
| Component | Weight | Rationale |
|---|---|---|
| Market Breadth | 30% | Most direct measure of market health |
| Sector Participation | 25% | Breadth of sector-level participation is critical for sustainability |
| Momentum | 20% | Direction matters as much as level |
| Sector Rotation | 15% | Rotation signals provide important risk-on/off context |
| Historical Context | 10% | Provides perspective but less actionable than real-time signals |
Limitations
- Data History: "all" data starts from Aug 2023; sector data from Jul 2024. Long-term percentile analysis is limited.
- Single Source: Relies entirely on Monty's dashboard (Finviz Elite data); no cross-validation with other breadth measures.
- No Volume Data: Uptrend ratio is price-based only; no volume confirmation.
- Lagging Indicator: 10-day moving average and slope introduce inherent lag.
- US Only: Covers US stocks only; no international market breadth.
- Sector Classification: Uses fixed GICS sectors which may not capture all rotation dynamics.
- Finviz Dependency: Upstream data depends on Finviz Elite availability and screener accuracy.
Complementary Analysis
This skill works best when combined with:
- Market Top Detector: For distribution day and leadership deterioration signals
- Technical Analyst: For index-level chart confirmation
- Sector Analyst: For detailed sector rotation analysis
- Market News Analyst: For fundamental catalyst context