Crypto Quant Trading: How a Structured Trend Model Works

Constructing a functional quant model in crypto often sounds like a highly technical pursuit reserved for institutional desks or seasoned quant researchers. Yet, over the past two weeks, I found myself gradually building one from scratch—one that reflects my personal trading style, prioritizes clear signals over noise, and focuses on sustainable long-term decision-making rather than impulsive short-term moves.

Today, the model produced its first complete set of outputs:

It’s far from a final product, but the process behind constructing this framework has already taught me a great deal about structure, discipline, and how to translate market understanding into quantifiable logic. More importantly, it offered clarity on how to approach trading in a way that fits my personality and risk tolerance.

Below is a breakdown of the core ideas underpinning this model, why certain choices were made, and what I believe matters most when building a practical quant approach for crypto trading.

Personal Style as the Foundation

Every trader has a different temperament, and any quant model that ignores this is doomed from the start. The strongest models are the ones that fit the trader, not the other way around.

I’ve always believed in three main principles:

Short-term, noisy signals may generate more frequent trades, but the cost is emotional strain. Anyone who has traded off intraday alerts knows the familiar experience: poor sleep, constant stress, and a mind anchored to price charts instead of real life. That’s not how I want to trade.

So the system was intentionally designed to follow a trend-trading philosophy:

1. Only 2–3 trades per month, on average
2. Prioritize medium-term trend clarity over daily volatility
3. Focus strictly on BTC or mainstream high-liquidity assets

This structure filters out the noise and maintains a calm pace. It forces decision-making to be based on structural trends rather than rapid fluctuations. The goal is simple: when the trend becomes clear enough—and only then—the model allows large positioning. If not, the safest position is patience.

Tracking Changes, Not Absolute Numbers

Crypto markets evolve constantly. Indicators only make sense when understood in relation to their context, not as static numbers.

For instance, many short-term indicators rely on delayed data, especially ETF flows, which often lag market movement by one day. Meanwhile, some long-term indicators—like stablecoin total market cap—tend to increase steadily over years, barely dipping even during bear markets. Looking at the absolute value on any given day tells you almost nothing about trend direction.

This is why all time-point indicators were transformed into relative measures:

• •
  Instead of yesterday’s stablecoin market cap, I track 30-day change
• •
  Instead of a single ETF flow number, I track net inflow/outflow relative to recent norms

This approach better captures momentum, sentiment, and underlying demand flows. Markets move on change, not on frozen snapshots. By quantifying relative behavior, the model aligns more naturally with how crypto cycles function.

Separating Direction, Position Size, and Entry Price

One of the most valuable lessons in this process was realizing that direction, position size, and price levels should not be tied together. Many traders unconsciously merge all three and end up with messy, emotional decisions.

To avoid that trap, the model treats them independently:

Direction

Using weighted scoring across long-term, mid-term, and short-term indicators, the model outputs one of three conclusions:

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  Long
• •
  Short
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  Trend unclear

Position Size & Leverage

Even when direction is clear, size must depend on the strength of the signal. A weak long is not the same as a strong long, even if both point upward.

The model therefore scales suggested exposure based on:

• •
  clarity of the trend
• •
  strength of the score
• •
  coherence across different timeframes

If clarity is low, the model defaults to a defensive posture—even if direction leans one way.

Entry Price

This is determined only when:

1. Trend direction is clear
2. Total score ≥ 60
3. Short-term indicators confirm a favorable entry zone

By separating these three components, the system avoids over-trading and prevents emotional decisions driven by price alone. Most importantly, it eliminates the illusion that one must always take a position. When the trend is unclear, the safest decision is simply to step aside.

Letting Formulas Handle the Complexity

The backbone of the model is a series of formulas spread across an Excel sheet. Although AI helped handle the formula writing, the logic behind every part was decided manually. This ensured:

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  accurate execution without logical errors
• •
  transparency in how each indicator contributes to the result
• •
  a stable and scalable structure that can be improved later

I provided indicators, weights, and logic, and AI generated the formulas accordingly. This preserved the model’s intended reasoning while accelerating the technical implementation.

In the end, the system became a practical tool that is easy to update daily:

1. Input raw data into the “Input” sheet
2. Dashboard automatically calculates the daily score and conclusion
3. Final results are copied into “History”

No matter how sophisticated a model appears, the real value is in consistent iteration—not in over-engineering.

Backtesting and Continuous Refinement

The final stage of building the model is rolling back and testing historical data once enough daily entries accumulate. Backtesting will reveal:

• •
  how well the model captures actual market swings
• •
  which indicators are over-weighted
• •
  whether thresholds need adjustment
• •
  how consistent the signals are across different cycles

Because the system stores a daily log in the “History” sheet, it will eventually allow me to evaluate its accuracy and stability over months of data.

If the results match expectations, the model can evolve into something more advanced—perhaps even:

• •
  a standalone local tool
• •
  a plug-in
• •
  or a fully automated system connected to APIs from various data sources

The structure is already there; now it’s about refining it through real performance.

A Personal Milestone and a Starting Point

When I began analyzing indicators half a month ago, I didn’t envision building a full-scale quant model. I simply wanted to understand the market more systematically. But writing daily insights forced me to organize my thoughts, refine my logic, and confront the inconsistencies in my own reasoning.

What started as a casual attempt to make sense of the market gradually turned into a full framework—a structured, logical approach aligned with my trading philosophy.

This model may not be perfect, and it may undergo many revisions. But it reflects the principles I believe in:

• •
  patience over noise
• •
  clarity over impulse
• •
  structure over emotion
• •
  trend over prediction
• •
  and discipline over market distraction

If backtesting shows strong performance, the next steps are clear: integrate data sources, automate the process, and turn this into a complete quant product.

But even now, in its early form, it already represents something valuable—a trading system rooted in self-awareness, experience, and a genuine understanding of how markets behave.

And that is often the most powerful edge any trader can have.

Past Indicator Studies

Long-term indicators:

The Rate-Cut Path Signals a Stronger Macro Cycle for Bitcoin Ahead

Medium-term indicators:

Maybe the Market Isn’t in a Bear Phase Yet

Bitcoin Faces ETF Selling Pressure, But It’s Not a Bear Market

Short-term indicators:

Bitcoin Turns Cautious as Key Indicators Point Downward

Crypto Still Bullish? VIX and ETF Flows Say It’s a Reset