Fixing the Momentum Filter

 

The Problem

I noticed the model was not making ANY trades for over a week. Every day, the model generated 30+ buy signals, but the portfolio manager - which acts as a gatekeeper - blocked ALL of them due to negative momentum.

Root Cause Analysis

1. Stale momentum data:

• Using articles from the last 10 days (now 3 days)
• Momentum was calculated from prices 5-20 days old
• By the time you made trading decisions, that momentum was ancient history

2. The momentum paradox:

• momentum_strength = lag_ret_5d - lag_ret_20d
• This measures deceleration, not absolute direction
• Example: Stock up 20% over 20 days, then pulls back 4% in last 5 days = -24% momentum_strength
• The filter was blocking strong stocks taking healthy pullbacks

The Backtest Evidence

Ran analysis on 31,600 test samples and found:

OLD filter (momentum_strength >= 0.1):

• Blocked 89% of stocks
• Blocked stocks: +0.64% avg return, 59.1% win rate ✅ BETTER
• Allowed stocks: +0.23% avg return, 55.7% win rate ❌ WORSE

Best performing cohort (which were blocking):

• "Strong deceleration" (<-0.15 momentum)
• Returns: +1.05% (1d), +2.58% (3d), +2.04% (5d)
• Win rates: 61.6%, 67.4%, 63.4%

The filter was blocking the best opportunities.

The Solution

OLD: Only buy if momentum_strength > 0

NEW: Buy if EITHER:

1. lag_ret_20d > 10% (strong 20-day uptrend), OR
2. 0 < momentum_strength < 15% (mild positive momentum)

Why this works (at least according to the backtest):

• Captures pullbacks in strong uptrends (mean reversion plays)
• Captures steady risers (not overextended)
• Blocks actual falling knives (negative long-term trend)
• Blocks momentum chasers at the top (>15% recent momentum)

Results

• 28/30 signals now pass the filter (vs 0/30 before)
• Deployed 9 trades today with the new logic
• All are strong stocks pulling back (exactly what backtest said to buy)

Now we wait to see if these actually perform as the backtest predicted.