Today I've passed the 50-point milestone in Math Academy's foundation maths track, covering

• linear regression and correlation

• grouped-data estimations (means, variances, standard deviation)

• inverses of exponential and logarithmic functions

To make this material "stick" i decided to write a short post connecting what i learned to crypto markets - demonstrating how even basic concepts show up in crypto markets - and why mastering maths compounds into edge.

Let us take a look at this chart:

This chart shows the 30-day rolling correlation between Bitcoin and the S&P 500. It whipsaws between negative and positive, often dramatically—currently sitting at -0.31.

That volatility isn’t noise. It’s narrative volatility.

At times, Bitcoin trades like a tech stock. At other times, like an inflation hedge. The correlation reflects changing beliefs more than changing fundamentals.

Key insight:

Correlation doesn’t predict the future—it reflects the present market mood.

But tracking rolling correlation tells you when sentiment shifts. That’s where opportunity lives.

The lesson from Math Academy was theoretical. This chart made it real.

Estimating population moments form binned data trains you to reason under partial information - the default regime in order books and OTC flows.

In the wild: This month alone, over $450M in token unlocks hit the market. Analysts don't model each address - they group daily unlock sizes, fit log-normal mixtures and forecast volatility clusters.

Key insight:

Every unlock calendar is a histogram of latent sell pressure. Treat it that way. Bin it, fit it, simulate it. Then estimate the impact on liquidity-adjusted returns!

Turning exponential decay into time - using

$t = \frac{ln(N_{0}/N)}{k}$

lets you calculate half-lives, break-evens and payback periods. you are not just seeing rates, you are measuring when things happen.

Shiba Inu's burn rate spiked 112,000% this week, removing 116M tokens from circulation. Traders who can invert the burn curve do not just cheer, they time when circulating supply hits key thresholds and price volatility accordingly.

Key insight:

Every burn or emission chart is a countdown clock. Solve for when supply drops 10%, 25%, 50% - then align those milestone with option maturities or liquidity pivots.

Follow Markov Drift for a biased walk through maths, code and markets!

written by kenkyuusha (https://x.com/0xKaze)

Today I've passed the 50-point milestone in Math Academy's foundation maths track, covering

• linear regression and correlation

• grouped-data estimations (means, variances, standard deviation)

• inverses of exponential and logarithmic functions

To make this material "stick" i decided to write a short post connecting what i learned to crypto markets - demonstrating how even basic concepts show up in crypto markets - and why mastering maths compounds into edge.

Let us take a look at this chart:

This chart shows the 30-day rolling correlation between Bitcoin and the S&P 500. It whipsaws between negative and positive, often dramatically—currently sitting at -0.31.

That volatility isn’t noise. It’s narrative volatility.

At times, Bitcoin trades like a tech stock. At other times, like an inflation hedge. The correlation reflects changing beliefs more than changing fundamentals.

Key insight:

Correlation doesn’t predict the future—it reflects the present market mood.

But tracking rolling correlation tells you when sentiment shifts. That’s where opportunity lives.

The lesson from Math Academy was theoretical. This chart made it real.

Estimating population moments form binned data trains you to reason under partial information - the default regime in order books and OTC flows.

In the wild: This month alone, over $450M in token unlocks hit the market. Analysts don't model each address - they group daily unlock sizes, fit log-normal mixtures and forecast volatility clusters.

Key insight:

Every unlock calendar is a histogram of latent sell pressure. Treat it that way. Bin it, fit it, simulate it. Then estimate the impact on liquidity-adjusted returns!

Turning exponential decay into time - using

$t = \frac{ln(N_{0}/N)}{k}$

lets you calculate half-lives, break-evens and payback periods. you are not just seeing rates, you are measuring when things happen.

Shiba Inu's burn rate spiked 112,000% this week, removing 116M tokens from circulation. Traders who can invert the burn curve do not just cheer, they time when circulating supply hits key thresholds and price volatility accordingly.

Key insight:

Every burn or emission chart is a countdown clock. Solve for when supply drops 10%, 25%, 50% - then align those milestone with option maturities or liquidity pivots.

Follow Markov Drift for a biased walk through maths, code and markets!

written by kenkyuusha (https://x.com/0xKaze)