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Strategy Lab / Day-Trade / BULLRAW

BULLRAW BullRaw v1.0 - Raw Momentum Control

Required comparison baseline: simply buying whatever is showing the strongest positive raw momentum today (no breakout level, no VWAP structure, no volume confirmation) using the exact same risk model and execution rules as BullPulse/BullFlow - answers whether their actual pattern logic adds value over a dumb 'buy what's up the most' rule. Version v1 - status SHADOW.

Equity
$9980.50
Closed Trades
5
Win Rate
0.0%
Evidence Status
Insufficient Evidence
N=5 closed trades

NOT a strategy - a comparison baseline. Ranks the day's tradable universe by today's raw open-to-now log return and buys the strongest movers, full stop.

Same entry window, risk model, sizing, and force-flat deadline as BullPulse - any performance gap is attributable to BullPulse's/BullFlow's actual entry logic, not a different execution model.

Deliberately excluded from the adaptive-learning layer (learning.py) - a control must stay a fixed, naive baseline for the comparison to mean anything.

Known limitations:

  • Synthetic bid/ask spread and 5-minute-bar execution resolution, same as every other engine here.
  • Uses a deliberately looser spread cap than the real engines (35bps vs 20-25bps) so it never smuggles in a hidden quality filter of its own.

This is a research hypothesis, not a proven profitable methodology. See docs/strategy_lab_day_trade_plan.md for the full repo-impact plan.

Evidence (Insufficient Evidence):

Needs at least 20 closed trades before any read is meaningful (5 so far). No significance testing is applied even above that floor - see module docstring for what's deferred (PBO, deflated Sharpe, bootstrap CIs).