momentum devandClaude Opus 5
45b0386347
Over 1,040 screened symbol-days (2026-08-01 on), asking which premarket
property predicts the session running >=50% open-to-high:
premarket range n avg run ran >=50%
>= 30% 201 34.7% 15.9%
15-30% 136 28.3% 14.7%
5-15% 303 15.2% 5.9%
< 5% 400 16.8% 4.3%
A name that already travelled >=15% premarket is 3.1x more likely to run
>=50% (15.4% vs 5.0%) -- the largest single lift in this dataset, and free:
it comes off minute bars the arm pass has already fetched.
Two findings constrain the shape:
- It is a STEP at 15%, not a gradient (15-30% and >=30% are 14.7 vs 15.9;
5-15% and <5% are 5.9 vs 4.3). So the multiplier is a threshold. A linear
scale would invent precision the data does not support.
- Premarket VOLUME carries NO signal (10.0/4.2/7.9/9.0% across participation
buckets, non-monotonic) and raw premarket volume runs inversely to the
outcome. scanner_snapshots.premarket_volume being NULL costs nothing;
range is the field that mattered all along.
Do not expect 3x. Replaying armed-set selection over the same 29 sessions,
top-5 by mean/median run and >=50% runners captured out of 145 slots:
gap x rvol (baseline) 39.77% / 10.97% 27
x3.0 if PM range >= 15% 42.00% / 13.16% 32
HARD FILTER >= 15% 37.27% / 12.67% 24 <- worse
PM range alone 32.86% / 12.65% 20 <- much worse
gap x rvol already encodes much of the same information, so the marginal gain
is ~+2 points of mean run and 4-5 extra big runners per 145 slots. It is
BOOST-only for a reason: gating on this feature scored below baseline, and it
must never replace gap/rvol. Every threshold in 10-20% and gain in 1.5-5.0 beat
baseline -- a flat plateau, so not fitted. The shipped 15%/3.0x are the measured
step and the measured lift, not the sweep's peak cell.
Fail-open: an unknown range yields a neutral 1.0, never a penalty.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01A2LepdJfPCfK5Q8uznjFBM
Deploying Momentum
Three supported shapes, one image/codebase. Pick by where it runs:
| Mode | Status | Use when | Docs |
|---|---|---|---|
| Docker Compose | ✅ canonical (production since 2026-07-09) | Any always-on Linux host; TrueNAS SCALE | docker/README.md, docker-compose.yml (root), docker/truenas-compose.yml |
| Local (systemd) | ✅ maintained | Development machine, WSL, bare metal without Docker | systemd/README.md + unit templates in deploy/systemd/ |
| Helm / Kubernetes | 🚧 planned refresh | Future k8s hosting | helm/ — the chart predates the 2026-07 architecture; starting point, not deployable |
Docker Compose (canonical)
The root docker-compose.yml runs the full stack self-contained: postgres 18,
trading loop, dashboard (:8765), admin panel (:8767), telegram bot, transcripts
pipeline, nightly scheduler, on-demand jobs, optional Grafana. The image is
published to docker.io/fez5587/momentum-app and ghcr.io/fez5587/momentum-app
by CI on every master push.
For TrueNAS SCALE use docker/truenas-compose.yml (pull-only, dataset host
paths, inline env). Read the gotchas in docker/README.md before editing the
YAML — anchor expansion, per-service env, and path case-sensitivity have each
caused an outage.
Local (systemd user services)
The original deployment: unit templates in deploy/systemd/ running a git
checkout + venv, config from .env. Still the best loop for development —
tests, backtests, and research scripts run here. Keep the units disabled
while the compose stack owns production: two loops against one Alpaca account
create duplicate orders.
Helm (future)
deploy/helm/ holds the original chart (single app deployment + CronJobs). It
predates the service split, the session gate, and the env-flag inventory; the
refresh is tracked in GitHub issues. Until then, compose is the way.
Config in all modes
.env.example(committed, sanitized) is the full variable inventory and says which layer each belongs in.- Behavior flags that MUST be set explicitly in any deployment — silent
defaults bite:
TRADING_AUTO_APPROVE=1(else entries queue for manual approval),ALPACA_DATA_FEED=sip,OLLAMA_ENABLED=true(catalyst engine),FMP_API_KEY(float cache).TRADING_SESSION_ONLYdefaults ON in deployed configs (bars after-hours entries). - Runtime overrides: the admin panel writes
data/overrides.env, which every service loads over its baked env at boot (runtime/env_overrides.py).