10f4bcc15b
Two complementary additions to validate the minute-horizon alpha hypothesis at IBKR-realistic costs: 1. `alpha_baseline --decision-stride N`: emits a new action every N steps; between decisions force action=0 (wait) so an open position is held rather than re-decided per bar. Cuts per-bar trade counts ~stride× and removes the coin-flip overtrading. Local 2Q sweep showed stride=200 + scaled training (8K episodes × 25 envs × H=1200) flipped Sharpe at ¼-tick from -4.29 (per-bar, 3-fold mean) to +1.78, with std collapsing from ±8.8 to ±1.15. Break-even cost moved from <¼-tick to ~1-tick — for the first time positive at IBKR-realistic passive-execution frictions. 2. `alpha_train_stacker --max-rows N`: optional cap on bars consumed from the fxcache. Used during local 2Q smoke (--max-rows 4M against the 17.8M-row 9Q fxcache) to fit Mamba2 training on a 4 GB consumer GPU; on the cluster (--no-cap) it sees all 9Q. 3. New Argo workflow `alpha-cv`: standalone template that compiles alpha_train_stacker + alpha_baseline + alpha_fill_coeffs.json, trains the stacker on the 9Q fxcache, then runs 9 sequential walk-forward folds of alpha_baseline on disjoint 1.9M-bar windows (one per quarter). Launcher script `scripts/argo-alpha-cv.sh` mirrors argo-train.sh conventions. The local 2Q test that motivated this commit is summarised inline in the alpha-cv template comments; the verdict was "framing was the bug — once decision cadence matches the multi-minute alpha horizon, the strategy is positive at IBKR commission". Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
82 lines
2.7 KiB
Bash
Executable File
82 lines
2.7 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Submit the alpha-cv workflow: stacker train on 9Q + 9-fold walk-forward.
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#
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# Defaults match the validated 2Q config (decision-stride=200,
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# scaled training H=1200 × N_par=25 × 8K episodes).
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#
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# Usage:
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# ./scripts/argo-alpha-cv.sh # submit on current HEAD
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# ./scripts/argo-alpha-cv.sh --branch main
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# ./scripts/argo-alpha-cv.sh --decision-stride 100 --horizon 2000
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set -euo pipefail
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SHA=HEAD
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BRANCH=main
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DECISION_STRIDE=200
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HORIZON=1200
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N_TRAIN_PAR=25
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N_TRAIN_EPISODES=8000
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FOLD_WINDOW=1900000
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GPU_POOL=ci-training-l40s
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WATCH=false
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usage() {
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cat <<EOF
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Usage: $0 [OPTIONS]
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--sha <commit> Git SHA (default: HEAD)
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--branch <branch> Git branch (default: main)
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--decision-stride <n> Decisions every N bars (default: 200)
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--horizon <n> Episode length in bars (default: 1200)
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--n-train-par <n> Parallel envs during training (default: 25)
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--n-train-episodes <n> Total training episodes (default: 8000)
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--fold-window <n> Bars per fold window (default: 1900000)
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--gpu-pool <p> GPU pool (default: ci-training-l40s)
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--watch Follow logs via argo watch
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EOF
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}
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while [[ $# -gt 0 ]]; do
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case "$1" in
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--sha) SHA="$2"; shift 2 ;;
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--branch) BRANCH="$2"; shift 2 ;;
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--decision-stride) DECISION_STRIDE="$2"; shift 2 ;;
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--horizon) HORIZON="$2"; shift 2 ;;
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--n-train-par) N_TRAIN_PAR="$2"; shift 2 ;;
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--n-train-episodes) N_TRAIN_EPISODES="$2"; shift 2 ;;
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--fold-window) FOLD_WINDOW="$2"; shift 2 ;;
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--gpu-pool) GPU_POOL="$2"; shift 2 ;;
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--watch) WATCH=true; shift ;;
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-h|--help) usage; exit 0 ;;
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*) echo "Unknown option: $1"; usage; exit 1 ;;
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esac
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done
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case "$GPU_POOL" in
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ci-training-l40s) SM=89 ;;
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ci-training-h100*|ci-training-h100x2|ci-training-h100-sxm) SM=90 ;;
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*) echo "Unknown gpu-pool: $GPU_POOL"; exit 1 ;;
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esac
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echo "Submitting alpha-cv workflow..."
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echo " branch: $BRANCH"
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echo " sha: $SHA"
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echo " decision-stride: $DECISION_STRIDE"
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echo " horizon: $HORIZON"
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echo " n-train-par: $N_TRAIN_PAR"
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echo " n-train-episodes: $N_TRAIN_EPISODES"
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echo " fold-window: $FOLD_WINDOW"
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echo " gpu-pool: $GPU_POOL (sm_$SM)"
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argo submit -n foxhunt --from=wftmpl/alpha-cv \
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-p commit-sha="$SHA" \
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-p git-branch="$BRANCH" \
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-p cuda-compute-cap="$SM" \
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-p gpu-pool="$GPU_POOL" \
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-p decision-stride="$DECISION_STRIDE" \
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-p horizon="$HORIZON" \
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-p n-train-par="$N_TRAIN_PAR" \
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-p n-train-episodes="$N_TRAIN_EPISODES" \
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-p fold-window="$FOLD_WINDOW" \
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$($WATCH && echo "--watch" || echo "")
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