u/krndude002

I want to run a live virtual experiment with a small group trading the same 15-minute BTC market.
The setup:
• Everyone starts with exactly $500
• We watch the same BTC15 round together
• Analyze the Up/Down pricing and order flow as a group
• Agree on an entry strategy before the round
• Enter based on the same signal/time window
• Watch how the market reacts in real time
• Coordinate an exit window and compare everybody’s results afterward
The point is to test whether a group watching the same data and following one strategy can make better entry/exit decisions than individuals trading independently.
This is not a guaranteed-profit thing. $500 is real risk capital if you’re trading live, and everyone remains responsible for their own trades. If people would rather test it first, we can run the first session with simulated $500 accounts.
I’m also testing a BTC15 analytics tool that tracks market movement and attempts to identify potential entry and exit windows, so the session would give us real data to evaluate how well the signals perform.
If you’re interested, comment or DM me “BTC15”.
Looking for a small group for the first session.

I've been building an experimental BTC/USDT microstructure model and would appreciate criticism from people with more experience in market microstructure and short-horizon modeling.

The original idea was fairly simple: classify aggressive executions by size and examine small-order flow immediately before and after unusually large executions.

I've informally called these groups “ants” and “whales,” but the terminology isn't important. The actual question is whether the interaction between aggressive volume, execution size, subsequent order flow, and realized price displacement contains information that isn't captured by conventional short-horizon momentum/volatility measures.

One observation motivated the project:

A large aggressive execution doesn't necessarily produce a large price movement.

For example, suppose 1 BTC of aggressive selling produces almost no downward movement, while a subsequent 0.3 BTC of aggressive buying produces a substantially larger positive displacement.

Raw signed volume would characterize the first event as stronger.

The price response suggests otherwise.

So I've been experimenting with a simple concept I'm calling displacement efficiency:

https://preview.redd.it/6zxstvgvsfrh1.png?width=180&format=png&auto=webp&s=bc9750ebc53eb50151459d04376ab1f142e41591

with direction incorporated when appropriate.

I'm particularly interested in the change in marginal displacement efficiency.

Consider a sequence where aggressive selling remains elevated but:

https://preview.redd.it/g30rmfb3tfrh1.png?width=137&format=png&auto=webp&s=ad1d9e057b78f6fe3e866db5cccbd9f7963ddff1

while price becomes pinned.

If aggressive buying subsequently begins producing increasing positive displacement,

https://preview.redd.it/4dsf5ue0tfrh1.png?width=114&format=png&auto=webp&s=25fbb1387b53b61940c5188657e13ef3bcfdcd5a

there may be a short-horizon regime transition before aggregate signed volume itself changes direction.

I'm currently separating aggressive flow into several size classes and identifying unusually large individual executions and same-direction execution clusters.

For each large event, I'm constructing event windows such as:

https://preview.redd.it/ozoi8ol5tfrh1.png?width=317&format=png&auto=webp&s=8bbe8c9fb62c1e9335aaefea56ed0179be9e4827

and measuring pre/post-event:

• aggressive buy/sell volume and notional imbalance
• execution frequency/count imbalance
• small-order acceleration
• price displacement per unit volume
• maximum favorable/adverse excursion
• displacement retention
• level traversal
• reversal vs. continuation

I'm also classifying the temporal relationship between large and small executions.

For example:

https://preview.redd.it/dl0nr2k8tfrh1.png?width=727&format=png&auto=webp&s=94f406ce8ef6062e440d3aba642e0cbfdca1fdc1

is treated differently from:

https://preview.redd.it/o8hfer3atfrh1.png?width=635&format=png&auto=webp&s=7bc2fcff24e6d17ec0130d5531e8ffca23975652

and both differ from:

https://preview.redd.it/ec04z4bbtfrh1.png?width=386&format=png&auto=webp&s=cd2c2f3cc3e41c0d3259e06eabcf50b493cbb5e0

The latter is particularly interesting.

Repeated aggressive volume with declining marginal displacement could potentially indicate passive absorption, although I'm careful with that interpretation because trades alone don't identify the passive participant or establish the presence of an iceberg.

I've also found highly repetitive execution-size/interarrival-time patterns in the Binance aggregate trade stream.

Rather than assuming these represent individual participants, I'm treating them as anonymous execution signatures and testing whether clusters defined by quantity distribution, interarrival time, burst structure, direction and price-level traversal exhibit persistent behavior.

The eventual dependent variable I'm interested in is very short horizon BTC movement, initially around 15 minutes.

One experimental feature representation currently looks roughly like:

https://preview.redd.it/0qh6411etfrh1.png?width=274&format=png&auto=webp&s=e1506de4c5f9133e04c9aab27253be0eefe18d50

where Iv is small-flow volume imbalance and IN is count/frequency imbalance.

I'm then considering additional state variables for:

https://preview.redd.it/30yv4gw1ufrh1.png?width=460&format=png&auto=webp&s=a0113ef68d49e5efcf1ba3778ca0ce632a96dbce

One interaction I'm particularly interested in is:

https://preview.redd.it/zcl37lc3ufrh1.png?width=239&format=png&auto=webp&s=bdf8ff96f99ba396999cc99c471f7e961e7b36e5

The intuition is that persistent aggressive flow isn't particularly informative if it produces no displacement, while displacement following synchronized flow may represent a different liquidity state.

I'm not claiming predictive power at this point.

That's actually why I'm posting.

My concern is that this kind of event analysis is extremely vulnerable to data mining and retrospective storytelling. It's easy to find convincing-looking examples of “absorption,” “whale leadership,” etc. after knowing the subsequent price path.

I'm therefore moving toward automatically recording the feature vector and prediction before each interval resolves, freezing model versions, and evaluating every interval rather than selecting examples.

For probability forecasts I'm planning to track calibration/Brier score in addition to directional accuracy and compare against simpler baselines such as momentum, realized volatility, signed-volume imbalance, and eventually a market-implied probability where available.

A few questions for people who work with microstructure data:

1. Is displacement per unit aggressive volume already captured by an established microstructure measure I'm effectively reinventing?

2. What would be the strongest null model here? Randomized trade signs? Event-time permutation? Matched volatility/volume regimes? Simple OFI?

3. How would you normalize displacement efficiency across volatility and liquidity regimes without destroying the information I'm trying to capture?

4. Is conditioning on large executions likely to introduce selection/endogeneity problems that make the apparent pre/post relationship misleading?

5. What would you consider convincing evidence that post-large-trade small-order acceleration contains incremental information rather than simply reflecting momentum already underway?

I'm less interested in whether the terminology/model sounds plausible and more interested in how you would try to falsify it.

If the methodology survives those tests, I'll continue developing it. If it doesn't, I'd rather find that out before optimizing the model around a spurious relationship.