Au2fut — CME Micro Futures Prop Edge Harness

Research only. No live trading, no execution code. This phase answers one question honestly: does a tradeable edge survive real futures costs and a prop firm's drawdown rules? Built on the discipline of the au2 / Au2qwen edge investigation (see docs/METHODOLOGY.md).

Why

au2 proved on BTC that a prop challenge is −EV by construction when no edge clears costs. Au2fut re-asks the question on CME micro futures (MES/MNQ/MGC) and futures prop firms (Topstep/Apex), where costs are a few $ per contract and equity indices carry real trend — but it refuses to build any trading infra until the edge is proven net of costs, out-of-sample and forward.

Install

pip install -r requirements.txt
cp .env.example .env      # research-only; no live keys

The pipeline

data/fetch.py        bars — pluggable source (yahoo|databento|ibkr|csv), cached
  └─ core/instruments.py    exact tick value + $ cost model (env-overridable)
       └─ diagnostics/edge_scan.py   breakout net $/contract verdict
       └─ diagnostics/mr_session.py  session mean-reversion (pre-registered, OOS)
            └─ diagnostics/oos_validate.py   train/test + walk-forward OOS
                 └─ core/prop_rules.py    Topstep/Apex trailing-DD machine
                      └─ diagnostics/prop_mc.py   P(pass), EV (--strategy mr|breakout)

Data sources (set FUT_DATA_SOURCE)

Source	Depth	Setup
yahoo (default)	intraday ~60d (tiny OOS)	none
databento	CME Globex minute/tick, multi-year	pip install databento, DATABENTO_API_KEY
ibkr	IBKR historical (pacing-limited)	pip install ib_async, TWS/Gateway running
csv	whatever you export	data/csv/<SYM>_<interval>.csv or FUT_CSV_PATH

Sub-hourly bars (5m/15m/30m) for databento/ibkr are aggregated from 1m, aligned to midnight UTC so RTH filtering stays correct. All diagnostics are source-agnostic — switching FUT_DATA_SOURCE changes nothing in the strategy code. The whole point: re-run the MR OOS verdict on years of multi-regime data the moment you plug in Databento/IBKR.

Run it

One command, any strategy, honest out-of-sample verdict (never an in-sample number):

python validate.py edge   MES 5m 1y        # Donchian breakout
python validate.py mr     MES 5m 1y        # session mean-reversion
python validate.py spread MES MNQ 5m 1y    # cointegrated spread MR

For deep multi-regime data (the only way to trust the verdict), set the source:

# PowerShell:  $env:FUT_DATA_SOURCE="databento"; $env:DATABENTO_API_KEY="db-..."
FUT_DATA_SOURCE=databento DATABENTO_API_KEY=db-... python validate.py mr MNQ 5m 1y

Lower-level tools the CLI wraps:

python -m data.fetch MES 1h 60d                                   # sanity-check data
python -m diagnostics.edge_scan  MES 1h 60d                       # in-sample sweep (context)
python -m diagnostics.prop_mc    MES 5m 1y topstep_50k --strategy mr --contracts 2
python -m pytest tests/ -q                                        # trust the rule engine

First read — and why it did NOT survive honest OOS

In-sample (edge_scan, whole window) looked encouraging:

Metric	In-sample result
MES 1h Donchian	positive across ~22/24 configs, best ~$48/trade PF 1.54
MNQ 1h Donchian	strongly positive (~$21k/yr/contract best)
Topstep 50K P(pass) @ 2 ct (prop_mc)	~59%

Then oos_validate.py (select params on train, trade held-out test) deflated it:

Test	OOS result	Verdict
MES 1h walk-forward	net_mean −$0.19, PF 1.00 (n=20)	edge gone — in-sample was a fit
MNQ 1h walk-forward	net_mean +$75, PF 1.39 (n=18)	weakly positive, too thin
MES/MNQ/MGC 1d 2y	n=4–12 per slice, signs flip	noise — inconclusive

Pushing Yahoo to its limit (5m/60d ≈ 13k bars) gave a statistically real OOS sample — and it was conclusive:

Instrument	TF	OOS n	net_mean	PF	win%
MES	5m	162	−$7.88	0.73	31%
MNQ	5m	160	−$15.38	0.80	41%
MES	30m	33	−$6.61	0.93	30%
MNQ	30m	37	−$59.51	0.73	30%

Verdict (Donchian breakout, MES/MNQ intraday, this data): no edge. Every TF collapses OOS — TRAIN PF 1.4–19, TEST negative, the textbook overfit signature. The n=162 5m sample is large enough to trust. The cheap futures cost structure did NOT rescue it because the signal itself is non-predictive intraday — the same thing au2 found for the seconds-scale BTC signal. The in-sample 59% prop pass-rate was a complete mirage.

Scope of this verdict: it kills the breakout hypothesis on these instruments on this data — not "no edge of any kind exists." Testing more strategy families is possible but must be done OOS-first / pre-registered to avoid data-mining a false winner (test enough strategies and one looks great in-sample by luck). Deeper minute data (Databento/IBKR) would also let mean-reversion / session strategies be judged on hundreds of OOS trades — see docs/METHODOLOGY.md.

Session mean-reversion — the first hypothesis to SURVIVE OOS

diagnostics/mr_session.py (pre-registered: fade Bollinger extremes inside RTH, flat at close — the inverse of breakout). Anchored walk-forward, OOS:

Instrument	TF	OOS n	net_mean	PF	survives 2-tick slip?	3-tick?
MES	5m	68	+$5.67 → +$4.42	1.22	yes (+$4.42)	no (−$2.14)
MNQ	5m	90	+$3.13 → +$2.63	1.05	yes (thin)	—
MES	15m	29	+$16.76	1.86	—	—

On the shallow 60-71d Yahoo window this looked like the first thing across BTC and futures to survive a pre-registered OOS test — but deep data killed it.

Deep-data verdict (Databento, 1 full year, 5m) — MR EDGE IS DEAD

Instrument	OOS n	net_mean	PF	verdict
MES 5m 1y	184	−$5.76	0.84	rejected
MNQ 5m 1y	279	−$2.68	0.96	rejected

Both negative even in-sample (MES −$4.89, MNQ −$2.41 over 441/427 trades). The summer-2026 positive was a regime artifact — gone on a multi-regime year. The few euros of Databento bought certainty and saved a real-capital deposit. This is the same verdict au2 reached on BTC: no edge clears costs. The line below about "thin but real" applied only to the shallow window and no longer holds.

(Historical note — the shallow-data reading that did NOT survive:) the edge was ~3-4 ticks gross and died at 3-tick slippage; fading the open is where slippage is worst.

Does it pass a prop challenge? No, not reliably (prop_mc --strategy mr, MES 5m, Topstep 50K, 2-tick slip): P(pass) ~0% at 1 ct, ~5% at 2 ct, ~28% at 4 ct — but 4 ct carries a 64% blow-up rate. The thin edge can't both hit a fixed-$ target and survive a fixed-$ trailing drawdown at the size required. Same structural wall au2's crypto prop_mc found.

Where it could matter: a personal account (no deadline, no trailing DD, small size, slow compounding) — not "fast cash," and only if real execution slippage stays ≤ 2 ticks. Mandatory next steps before any money: deeper minute data across multiple regimes, and forward live-paper measuring actual fill slippage on the open fade.

Layout

Au2fut/
├── core/
│   ├── instruments.py   CME micro specs + $ cost model
│   └── prop_rules.py    Topstep/Apex trailing-DD / daily-loss / target engine
├── data/
│   └── fetch.py         Yahoo bar fetcher (cached); swap for Databento/IBKR later
├── diagnostics/
│   ├── edge_scan.py     vol-gated Donchian backtest, net $ verdict
│   └── prop_mc.py       Monte Carlo P(pass)/EV through the prop rules
├── tests/               prop-rules engine tests (9, all green)
└── docs/METHODOLOGY.md  the discipline, ported from au2

Status / next steps

• Honest $ cost model + prop-rules engine (tested)
• Edge-scan + prop Monte Carlo, runnable on real data
• OOS validation (oos_validate.py) — in-sample edge did NOT survive on free data
• Deeper data: minute bars w/ years of history (Databento/IBKR) → swap data/fetch.py
• Re-run OOS for a statistically meaningful sample (target ≥ 100 OOS trades)
• Calibrate FUT_RT_COMMISSION to a real prop plan fee schedule
• Forward live-paper to confirm backtest net $/trade
• Only then: executor + prop-risk guard (port from au2 prop_guard.py)