INDEPENDENT AUTOMATED TRADING LAB

Build. Test.
Publish the result.

We run automated trading systems on forward market data and document what actually happens—including failures. No cherry-picked backtests presented as proof.

Explore live researchResearch methodology ↗
PAPERForward experimentsVersion history preserved
REPLYOPS / LABFORWARD TEST
ACTIVE EXPERIMENTV6 / FreqAI
PAPER
+$10.42 REALIZED PAPER PNL
CLOSED5OPEN2WIN RATE*100%
BTC / ETH / SOL · 5M · AUTO RETRAIN
SYSTEMS RUNNING3
EVIDENCE MODEPAPER
PUBLIC METRICSAUTOMATED
RESULT POLICYNO CHERRY-PICKING
LIVE RESEARCH

Three systems. One forward record.

All experiments →
CONTROL PAPER

V4

+$2.98REALIZED PNL
17CLOSED1OPEN41%WIN RATE
ADAPTIVE PAPER

V4.4

+$0.00REALIZED PNL
0CLOSED1OPEN—WIN RATE
FREQAI PAPER

V6

+$10.42REALIZED PNL
5CLOSED2OPEN100%WIN RATE

Current samples differ substantially. These cards describe recorded PAPER results; they are not a ranking of strategy quality.

HOW IT WORKS

From market data to public evidence.

Trading infrastructure stays separate from the public website. Only sanitized metrics leave the trading environment.

01Market dataBTC · ETH · SOL
→
02Strategy / MLVersioned systems
→
03PAPER executionForward observation
→
04Public metricsSanitized feed
RESEARCH NOTES

Useful when the bot does not work.

FAILURE ANALYSIS

Why trading bots fail

Seven failure modes across research, execution, risk and operations—and how to diagnose them without optimizing the evidence away.

Read analysis →
EVIDENCE

Backtest vs PAPER

Why the two stages answer different questions.

MACHINE LEARNING

FreqAI training

Windows, retraining and future leakage.

INFRASTRUCTURE

Secure a trading VPS

Reduce credential and server exposure.

THE RULE

If a system fails, the result stays.

Winning experiments are easy to publish. Failed versions are more useful. ReplyOpsAI preserves version history and keeps BACKTEST, PAPER and LIVE evidence explicitly separate.

Read the methodology