u/Kai8250

My last post got a lot more attention than I expected, and quite a few people asked what I was going to do next. A few also asked if I could share at least some of the strategy.

The original backtest ended on July 31. I'm leaving the strategy exactly as it was at that point and using the full month of August as a forward test.

I haven't looked at the August data or used any of it to adjust the strategy. No parameter changes, no new filters, no fixing things after seeing the result.

I'm not just adding August to the original Jan-Jul backtest either. I'm running it separately through the forward-testing side, and that's giving me a technical problem at the moment. Processing the full universe at 1-second resolution is running me out of memory. I'm working on that now, so I don't have the August result yet.

The run will include the corrected broker costs, fees and slippage assumptions as well.

I still don't want to post the exact rules or parameters, but I can explain a little more about how the strategy works.

RVOL comes first, but RVOL itself isn't the entry signal. I use it to judge the current state of the stock and narrow down what the strategy should be paying attention to.

From there I'm watching the 1-second data for a specific value. When that value breaks a certain level, that's what triggers the buy. It's something I'm specifically measuring at 1-second resolution, which is also why this strategy isn't based on minute bars.

The exit is a little different.

Once I'm in a position, I divide the stock's behavior into several states. Those states are checked continuously after the entry. If the stock moves out of the state where I want to keep holding it, the strategy exits immediately.

So RVOL gets me to the stocks I'm interested in, the 1-second data determines the actual entry, and what happens after the entry determines when I get out.

That's about as much of the strategy as I want to give away for now.

Once I get the memory problem sorted out, I'll run August without touching the strategy and post the numbers. Good or bad.

RVOL is probably one of the most common filters used by momentum day traders.

The logic sounds obvious: unusually high volume means unusual attention, so maybe the highest-RVOL stocks are more likely to keep running.

I tested that using 388 trading days of US equity data, focusing on already-active stocks between 9:30 and 10:00 ET.

I split 6,313 observations into five RVOL groups and looked at what happened over the next 10 minutes.

The result surprised me.

Lowest RVOL quintile: 42.3% went up

Highest RVOL quintile: 41.9% went up

Basically no directional improvement at all.

But RVOL did predict something very strongly: movement.

In the highest-RVOL quintile, 52.2% moved at least ±5% within 10 minutes, compared with only 20.5% in the lowest quintile.

So high RVOL was very good at answering:

“Is this stock about to move?”

But not:

“Is this stock about to go up?”

And simply buying them wasn't attractive. The highest-RVOL group had a −1.45% median 10-minute return.

Using actual quotes — buying at the ask and selling at the bid 10 minutes later — the average result was about −1.11% before trying to optimize anything.

One caveat: this isn't a market-wide RVOL study. The sample starts with stocks that were already active (RVOL ≥ 3), so the question is really whether even higher RVOL adds directional information among stocks already moving at the open.

My takeaway so far:

RVOL looks much more useful as an activity filter than as a long signal.

For those who trade high-RVOL momentum names: what do you combine with RVOL to decide direction?

I was looking at execution costs in low-priced US equities and found something I didn't expect.

Stocks below $1 can quote in $0.0001 increments, while $1–$5 stocks generally quote in $0.01 increments — a 100x difference in minimum tick size.

But the finer grid didn't produce tighter markets.

I sampled 38 regular trading sessions between April 2025 and July 2026 using one-second BBO recordings:

276,345 quoted seconds below $1 2,047,344 quoted seconds from $1–$5

Median spread:

Under $1: 1.81% $1–$5: 0.72%

So the group with the 100x finer grid actually had a 2.5x wider median spread.

The more interesting result was whether the minimum tick was actually binding:

$1–$5 stocks sat at their one-cent minimum 60.4% of the time Sub-$1 stocks sat at their $0.0001 minimum only 2.4% of the time The median sub-$1 spread was 96 minimum ticks wide

My interpretation is that once the tick becomes small enough, it stops constraining the spread. At $0.50, one $0.0001 tick is only 2 bp, so the market can quote dozens or hundreds of ticks wide.

That also raises an execution question: improving a $0.50 bid by one tick costs almost nothing, so stepping ahead of resting limit orders is extremely cheap.

I haven't tested actual fill quality yet — this is quote data, not fill data.

For anyone modeling passive execution in sub-$1 stocks: how do you handle queue position? Explicit queue modeling, empirical fill probabilities, or something else?

I used to think a backtest falling apart from slippage would be obvious.

Lower win rate. More losing trades. Maybe a few trades that no longer hit the target.

That's not what happened.

We took a completed day-trading backtest with 2,084 trades across 705 US stocks and 75 out-of-sample sessions and reran the exact same trades.

Same entries. Same exits. Same signals. Same trade selection.

The only thing we changed was the assumed execution price.

The baseline booked entries and exits at the touch. Then we moved every fill against the trade by one tick.

One tick removed 44% of the edge.

Two ticks removed 87%.

What surprised me wasn't the P&L drop. Obviously worse fills reduce P&L.

It was what didn't change very much:

the win rate.

The strategy still looked like it was “working.” It was still producing winners. The equity curve didn't immediately scream that something fundamental was wrong.

The average trade had simply become a little worse.

And a little worse, repeated 2,084 times, was enough to destroy most of the expectancy.

That changed how I look at backtest results.

A strategy can have a respectable win rate and still have almost no room between its theoretical edge and the friction required to trade it.

So now, when I look at a backtest, one of the first numbers I want isn't the win rate or even the total return.

I want to know:

How much worse can every fill get before the strategy stops making money?

If the answer is “one tick,” I'm not sure I'd call that a robust edge anymore.

I'm curious how other day traders handle this.

For those of you who backtest and then trade live: how much execution deterioration do you require a strategy to survive before you trust it?

One tick? A fixed number of basis points? Actual measured live slippage? Or do you use something else entirely?

You can be having a long day, then some random person holds a door, makes a silly joke in an elevator, compliments your shirt, or lets you go ahead of them at the grocery store.

They probably forget about it thirty seconds later.

Meanwhile, you're still thinking about it hours later.

I like the idea that we've probably made someone's day a little better without ever knowing we did.

What's a tiny interaction with a stranger that stuck with you way longer than you expected?

I was thinking about this today. When I was younger, a year felt like forever. Now I blink and somehow another month is gone.

It’s especially weird when something I remember as “a couple years ago” turns out to have happened 5 or 6 years ago.

Does anyone else get this feeling? What’s something that made you suddenly realize how fast time is passing?

I have a 2021 X5 M50i with around 60k miles and I'm starting to go through some preventative maintenance.

I'm planning on doing the front and rear differential fluids, transfer case fluid, and eventually the ZF transmission fluid/filter pan.

For those of you with higher-mileage M50i's, what did you replace or service around 60k–70k miles?

Anything you wish you had done earlier?

I do most of my maintenance myself, so I'd also be interested in hearing what you DIY'd versus what you decided to leave to a BMW shop.

While building a point-in-time research pipeline for a dynamic US equity universe, we found several issues that had passed our initial data-integrity checks.

The most important ones were:

Universe survivorship: data collection stopped when a symbol left the ranked universe. Entries were valid, but forward evaluation became conditional on remaining in the universe.

Backfilling missing features: bfill silently introduced values that were not available at decision time, particularly around premarket/regular-session boundaries.

Data/live contract mismatch: some fields existed in reconstructed historical data at timestamps where the live system would not have received them yet.

Non-causal ranking: selecting the top X% using the score distribution of the entire evaluation period. Fine for analysis, impossible to reproduce live.

What bothered me is that conventional integrity checks didn’t necessarily catch these. Row counts matched, calculations reproduced, timestamps looked correct, and there was no obvious label leakage.

The failure was at a different level: could the live system actually have known this value, selected this symbol, and made this decision at that exact time?

We’ve since started treating that as a separate validation layer rather than assuming a clean dataset implies a causal backtest.

For those running dynamic universes: how are you auditing historical/live information parity?