Michael Steinbach

Building algorithmic trading systems in public. Real capital, real losses, real wins - all documented. ↓

[Michael Steinbach @BituraiTrading]

Bitcoin will not be down forever.

I know, a lot of people are panicking.

Bit to be honest, when you can not hold for a few months, why have you invested?

And when you are a trader like me, why the fu*k you are complaining ?

This is just a great short.

[Michael Steinbach @BituraiTrading]

@koritual Awesome! Great development for all European !

[Michael Steinbach @BituraiTrading]

See a lot of panic on crypto these days.

Take a 🍿and relax.

[Michael Steinbach @BituraiTrading]

Massive Volume coming in The Bitcoin Market today.
I am happy to get my trades. See my Setups on my last post here. NFA

[Michael Steinbach @BituraiTrading]

The result is a high-fidelity forensic log explaining why each of the 972 strategies passed or failed. This enables systemic validation of model behavior across all market regimes, isolating silent failures from intended patience.

[Michael Steinbach @BituraiTrading]

The most critical observability isn't always in the live production system. It's often in the simulation layer that selects what goes into production.

[Michael Steinbach @BituraiTrading]

Instrumenting the simulation engine to log the specific rejection reason for every signal is the solution. This exposes internal state metrics for each strategy, mirroring the observability of production systems within a sandboxed environment.

[Michael Steinbach @BituraiTrading]

@mert Congrats on that

[Michael Steinbach @BituraiTrading]

This yields a complete decision log, not just a trade history. Analysis reveals the true ratio: for every executed trade, hundreds of signals are evaluated and filtered. This data becomes a direct input for systematic filter calibration and performance tuning.

[Michael Steinbach @BituraiTrading]

A trading bot's performance is defined more by the trades it rejects than the trades it takes.

[Michael Steinbach @BituraiTrading]

Instrumenting the signal engine to write every evaluation to a dedicated event pipeline solves this. Each potential entry should log its raw state, the filters applied, and a specific rejection reason (e.g., `filter_blocked`, `insufficient_data`, `adx_below_threshold`).

[Michael Steinbach @BituraiTrading]

The outcome: Full historical drill-down capability is achieved for analytics. The live production engine remains untouched, with zero new code paths and zero added risk to its core operation. Analysis is decoupled from execution.

[Michael Steinbach @BituraiTrading]

A live trading engine's code is sacrosanct. For historical analysis, it must not be modified. Not by a single line.

[Michael Steinbach @BituraiTrading]

The solution: A 1:1 clone of the production engine is created for offline analysis. This clone is modified to add the required timestamp tracking. A parity test confirms its PnL output is bit-for-bit identical to the live engine across all exit modes.