Market research workbook
Market Trend Probability Indicator
I built this workbook to answer one recurring question: when market signals disagree, what does the overall setup actually say?
What I didIndicator selection, scoring logic, and historical testing
Built withMulti-signal scoring, Forward testing, Correlation analysis
What came out of itThe finished workbook gave me one place to compare 15 inputs, see the composite reading, and check how it changed over time.
MTPITotal market cap
- Snapshot
- Jan 17, 2025
- Composite
- −0.91, Short
| Category | Indicator | Frame | Score | Result |
|---|---|---|---|---|
| Perpetual | EWMA | 3D | −1.00 | Bearish |
| Perpetual | SALMA RED K | 3D | −1.00 | Bearish |
| Perpetual | Michaels EMA | 2D | −1.00 | Bearish |
| Perpetual | HSMA | 4D | −1.00 | Bearish |
| Perpetual | T3 Striped [Loxx] | 4D | −1.00 | Bearish |
| Oscillators | Regularized MA suite | 2D | −1.00 | Bearish |
| Oscillators | Normalized KAMA | 4D | −1.00 | Bearish |
| Oscillators | Sebastine Trend Catcher | 2D | −1.00 | Bearish |
| Oscillators | Kalman Hull RSI | 3D | −1.00 | Bearish |
| Oscillators | Trend Following MA’s | 3D | −1.00 | Bearish |
| Bitcoin | T3S | 5D | −1.00 | Bearish |
| Bitcoin | Michaels EMA | 4D | −1.00 | Bearish |
| Ethereum | Michaels EMA | 2D | −1.00 | Bearish |
| Ethereum | EWMA | 4D | −1.00 | Bearish |
| Macro | Correlation coefficient | 15-120D | 0.28 | Slight bull |
Inside the model
A composite score is only useful when I can see what is pulling it.
The matrix shows the raw readings. The gauge and charts show how I combined them and checked the result.15signal inputs
5research categories
24dated observations
10macro series reviewed
MTPIWhole market trend, total market cap
- Updated
- Jan 17, 2025
- Average
- −0.91, Short
| Category | Indicator | Timeframe | Comments / inputs | Score | Result |
|---|---|---|---|---|---|
| Perpetual | EWMA | 3D | 8 | −1.00 | Bearish |
| Perpetual | SALMA RED K | 3D | Length 10 | −1.00 | Bearish |
| Perpetual | Michaels EMA | 2D | 8 - 13 | −1.00 | Bearish |
| Perpetual | HSMA | 4D | 8 | −1.00 | Bearish |
| Perpetual | T3 Striped [Loxx] | 4D | Period 5 | −1.00 | Bearish |
| Oscillators | Regularized-moving-average oscillator suite | 2D | Length 3 / Reg. length 21 | −1.00 | Bearish |
| Oscillators | Normalized KAMA oscillator | 4D | 6 / 19 / 5 / 8 | −1.00 | Bearish |
| Oscillators | Sebastine Trend Catcher | 2D | 5 / 4 | −1.00 | Bearish |
| Oscillators | Kalman Hull RSI | 3D | 4 / 0.13 / 4 | −1.00 | Bearish |
| Oscillators | Trend Following MA’s | 3D | EMA 2 | −1.00 | Bearish |
| Bitcoin | T3S | 5D | 4 | −1.00 | Bearish |
| Bitcoin | Michaels EMA | 4D | 4 / 6 | −1.00 | Bearish |
| Ethereum | Michaels EMA | 2D | 5 / 12 | −1.00 | Bearish |
| Ethereum | EWMA | 4D | 4 | −1.00 | Bearish |
| Macro correlation | Correlation coefficient | 15D / 30D / 90D / 120D | N/A | 0.28 | Slight bull |
| MTPI total average score | −0.91 | Short | |||
Macro check
BTC correlation table
| Series | 15D | 30D | 90D | 120D | Avg. |
|---|---|---|---|---|---|
| SPX | -0.18 | 0.33 | 0.87 | 0.81 | 0.46 |
| NDX | 0.10 | 0.66 | 0.89 | 0.81 | 0.62 |
| DXY | -0.22 | 0.14 | 0.92 | 0.87 | 0.43 |
| Gold | -0.10 | 0.28 | 0.26 | 0.43 | 0.22 |
| US10Y | -0.01 | -0.02 | 0.80 | 0.70 | 0.37 |
| VIX | 0.41 | 0.30 | -0.43 | -0.31 | -0.01 |
| MOVE | -0.28 | -0.74 | -0.49 | -0.38 | -0.47 |
| Fed liquidity | 0.76 | 0.71 | 0.60 | 0.62 | 0.67 |
| PBOC liquidity | 0.75 | 0.27 | 0.65 | 0.65 | 0.58 |
| Global liquidity | 0.05 | -0.53 | 0.05 | 0.13 | -0.08 |
Score history
Total score
Jan 05Jan 14Jan 22Jan 30Feb 05
The line continues past the January 17 matrix. It tracks the model score, not investment performance.
This is an old research snapshot, not a live signal or a trading recommendation.
The question behind it
What I was trying to understand
Trend, momentum, oscillators, and macro correlations rarely line up perfectly. Looking at them one by one made it too easy to give one signal more weight than it deserved.The standard I set
What a useful answer needed
I wanted one process I could repeat without changing the rules to fit the market view I already had.My part
Where I did the work.
- I grouped trend signals, moving averages, oscillators, and macro correlations by what each one was meant to tell me.
- I created the scoring logic that rolls the individual readings into one market view.
- I tracked the score through time so I could see how quickly the model changed direction.
- I added cross-asset correlations to keep the composite reading in context.
Process
How I got to an answer.
Structure the inputs
I grouped the indicators by purpose so a trend signal was not treated the same way as a macro correlation.
Create a scoring layer
I converted the readings to a common scale and averaged them into a directional score.
Evaluate through time
I logged the score by date and compared it with the surrounding market and macro data.
Where judgment entered
- I separated the signal groups before combining them.
- I used the history to study the model's behavior, not to claim what markets would do next.
- I kept proprietary inputs and live readings out of the public version.
Takeaway
What stayed with me
The score only looks objective. Judgment enters when I choose the inputs and decide how to group them. The useful part was making those choices visible.