fulldeg

Oct 10–11 Flash Crash: what happened and what it means
Context

Late Oct 10 (US) into early Oct 11 (Europe) the market spiked lower, then snapped back. It is educational, not financial advice.

DYOR.

Why the drop accelerated
- Thin liquidity in a quiet window, plus crowded leverage.

- First support breaks triggered stops and forced liquidations that had to trade.

- Market makers widened or pulled quotes, spreads grew, perps dislocated from spot.

- BTC weakness spilled into ETH and high beta names where leverage was heavier.

- Once forced flow exhausted and depth rebuilt, prices rebounded toward prior references.
How to read a move like this

- Separate mechanical flow (stops, liquidations) from informational flow (new data that shifts fair value).

- Compare spot, perps, and dated futures. Brief perp overshoots that re anchor to spot suggest mechanics.

- Watch repair signals: spreads narrow, 10 to 50 bps depth returns, funding cools, open interest resets lower.

Risk notes

- Sizing first. Cap leverage per asset and per time window. Survival is the edge.

- Respect time of day. US evening and pre Asia can be thinner. Adjust size and stops.

- Avoid clustered tight stops. Consider buffers and staggered exits.

- Reduce single point of failure. Mix spot and perps, diversify venues, keep margin cushions.
What to monitor next

- Open interest and funding after the purge.

- Spot demand versus leverage rebuild.

- Order book resilience and fewer air pockets.

🤝 What is Pairs Trading?

Remember when I said I consistently make profits?

Here’s the why — and more importantly, the how.
Pairs Trading is a market-neutral strategy rooted in statistics and mathematics, not gambling, guessing, or gut feelings.

This is not luck.

This is a quantitative model based on data and logic.

🔍 How Does It Work?

Let me break it down simply:

Pick a Pair: Choose two highly correlated assets (e.g., ETH & ETC).

Monitor the Spread: Track the price difference (spread) between them.

Model the Relationship: Use statistical methods to determine the “normal” spread range.

Exploit Deviations:

When the spread deviates significantly from the norm,

go long on the undervalued asset and short the overvalued one.

Profit on Reversion:

As the spread returns to normal, the positions are closed — with a profit.

📐 Why It’s Not Gambling

This approach is:

✅ Backed by historical data
✅ Grounded in mathematics
✅ Systematic and repeatable
✅ Free from emotion and noise

I'm not just randomly buying and selling. I’m not chasing pumps.

I’m a mathematician and a quant, with backgrounds in both Mathematics and Economics, and soon starting my Master’s in Quantitative Finance.

This is research-backed strategy execution, not hope-based trading.

💡 In Short:

I’m not trading based on hunches.
I’m running a mean-reversion engine built on math.
And often — just like the stats say — it works.

👍 Like this post if you're curious.
📚 Because in the next ones, I’ll share:

How I select pairs

What cointegration and z-score really mean

A full working Pairs Trading script in Python

Stay tuned.

Stay smart.

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