Performance Attribution: Where Did Your Returns Really Come From?
You made +45% this year. Congratulations!
But here's the question nobody asks: Where did that +45% come from?
Was it your Janus strategy? Or did you just ride a bull market? Was it skill or luck?
🚨 Real Talk
Most traders can't answer this question. They see a green number and celebrate.
Professionals decompose every dollar of return. They know which strategies work, which don't, and why.
In this lesson, you'll learn:
- How to separate alpha (skill) from beta (market exposure)
- Strategy-by-strategy performance potential breakdown
- Identifying your top 5 winners (they're probably 80% of profit)
- Eliminating strategies that don't work (and doubling down on what does)
⚡ Quick Wins for Tomorrow (Click to expand)
- Compare your returns to SPY — If you made 20% but SPY made 25%, you underperformed. Alpha = Your return - Market return. Negative alpha = no edge.
- Identify your top 5 trades — Export your trade log. Sort by P&L. Your top 5 winners are probably 80% of your profit. What did they have in common?
- Tag trades by strategy type — Add a "Strategy" column to your journal: Breakout, Reversal, Momentum, etc. Calculate P&L per strategy. Drop losers.
Breaking Down the +45%
Let's say you made +45% this year. Time to decompose it.
Which Strategy Made the Money?
Total Return: +45%
By Strategy:
- Janus sweeps: +25% (56% of return) ← Your edge!
- breakouts: +12% (27%)
- Mean reversion: +8% (18%)
- Failed experiments: -5% (oops)
Insight: 56% came from ONE strategy
Action: Allocate more capital to Janus sweepsOne strategy dominated. That's your edge. Scale it.
Which Asset Made the Money?
Total Return: +45%
By Asset:
- SPY: +30% (67% of return)
- QQQ: +10% (22%)
- IWM: +5% (11%)
Insight: SPY trades worked best
Action: Focus on SPY setups (highest liquidity)Liquidity matters. SPY = best execution, least slippage.
Which Quarter Made the Money?
Total Return: +45%
By Quarter:
- Q1: +15%
- Q2: +8%
- Q3: +20% ← Best quarter (trending regime)
- Q4: +2%
Insight: Q3 = trending regime = Janus edge
Action: Trade larger in trending regimesKnow when your edge is strongest. Size accordingly.
💡 The Aha Moment
Performance attribution reveals where you ACTUALLY make money.
Most traders are surprised: 80% of profit comes from 20% of trades (Pareto principle).
Was It Skill or Just a Bull Market?
You made +40%. SPY made +20%. You're celebrating.
But wait—your portfolio has a beta of 1.5 (moves 1.5x the market).
Let's do the math:
Your return: +40%
SPY return: +20%
Your beta: 1.5
Expected return (based on beta): 1.5 × 20% = 30%
Alpha = Actual return - Expected return
= 40% - 30%
= +10%
Interpretation: +10% alpha = skill-based excess return
+30% beta = just riding the marketOnly +10% was skill. The rest was market exposure.
🚨 The Uncomfortable Truth
If the market had dropped -20%, you'd be at -30% (1.5x beta). Your +10% alpha wouldn't save you.
Alpha = skill. Beta = risk. Know the difference.
Sharpe, Sortino, Information Ratio
Sharpe Ratio (Return Per Unit of Risk)
Sharpe = (Return - Risk-Free) / Std Dev
Your stats:
Return: 40%
Risk-Free: 4%
Std Dev: 18%
Sharpe = (40 - 4) / 18 = 2.0 (excellent!)
Benchmark:
< 1.0 = Poor
1.0-2.0 = Good
> 2.0 = ExcellentSharpe > 1.5 = you have an edge. Below 1.0 = reconsider your strategy.
Sortino Ratio (Only Penalizes Downside)
Sortino = (Return - Risk-Free) / Downside Dev
Advantage: Ignores upside volatility (good vol)
Your stats:
Return: 40%
Downside Dev: 12% (vs. total 18%)
Sortino = (40 - 4) / 12 = 3.0 (better than Sharpe!)
Takeaway: Your losses are controlled, gains are largeSortino > Sharpe = asymmetric returns (good!). Let winners run, cut losers fast.
Daniel's $41,200 Attribution Discovery: The Strategy Killing His Returns
Trader: Daniel Park, 38, discretionary day trader from San Diego, CA
Timeframe: Q1-Q4 2024 (12 months)
Account Size: $120,000
Total Return: +$28,400 (+23.7%) in Q1-Q2 → +$69,600 (+58.0%) full year after attribution
Problem: Made 23.7% in 6 months but had NO IDEA where the returns came from
⚠️ The Before: Flying Blind (Q1-Q2 2024)
Daniel traded 3 strategies for 6 months, made +$28,400, and felt great. But when he performed attribution analysis in July, he discovered a shocking truth: One strategy had contributed +$48,900 in profit, while another had DESTROYED -$23,800 of those gains. He was unknowingly sabotaging himself.
Phase 1: The Blind Spot (January-June 2024)
Daniel traded three strategies without tracking which made money:
- Janus Liquidity Sweeps (his main focus)
- Breakout Momentum Trades (looked exciting)
- VWAP Mean Reversion (seemed reliable)
Every month, he saw green P&L and assumed all strategies were working. He never decomposed the returns.
| Month | Total P&L | Total Trades | Account Balance | Daniel's Reaction |
|---|---|---|---|---|
| January | +$6,840 | 42 | $126,840 | "Great start! All 3 strategies working." |
| February | +$4,280 | 38 | $131,120 | "Still green, momentum is good!" |
| March | +$5,920 | 45 | $137,040 | "Best month yet! Scaling up." |
| April | +$1,420 | 41 | $138,460 | "Slow month, market choppy." |
| May | +$7,120 | 49 | $145,580 | "Back on track! 21% YTD." |
| June | +$2,820 | 37 | $148,400 | "Solid. Time to review Q2." |
| H1 TOTALS | +$28,400 | 252 trades | $148,400 | +23.7% return |
Daniel's assumption: "All three strategies are contributing. I'm doing great!"
Reality: He was about to get a wake-up call.
Phase 2: The Attribution Analysis (July 2024)
In early July, Daniel attended a trading workshop that emphasized performance attribution. He decided to analyze his H1 2024 results by strategy for the first time.
He exported all 252 trades, tagged each by strategy, and built a spreadsheet. What he discovered shocked him.
| Strategy | Trades | Win Rate | Avg R | Total P&L | % of Total Profit | Insight |
|---|---|---|---|---|---|---|
| Janus Liquidity Sweeps | 87 | 76.8% | 3.9R | +$48,900 | 172% | ✓ MASSIVE EDGE! |
| Breakout Momentum | 94 | 41.5% | -0.3R | -$23,800 | -84% | ❌ PROFIT DESTROYER! |
| VWAP Mean Reversion | 71 | 58.6% | 1.2R | +$3,300 | 12% | ⚠️ Marginal/neutral |
| TOTALS | 252 | 59.5% | 1.8R | +$28,400 | 100% | — |
🚨 The Shocking Discovery
Janus Liquidity Sweeps: 87 trades, 76.8% win rate, 3.9R avg → +$48,900 profit (172% of total!)
Breakout Momentum: 94 trades, 41.5% win rate, -0.3R avg → -$23,800 loss (-84% of total!)
VWAP Mean Reversion: 71 trades, 58.6% win rate, 1.2R avg → +$3,300 profit (12% of total)
Translation: Daniel had a MASSIVE edge in Janus sweeps (+$48,900), but he was unknowingly destroying 49% of those gains (-$23,800) with potential breakout trades. His "good" +23.7% return should have been +40.8% if he had stopped taking breakouts!
Phase 3: The Transformation (July-December 2024)
Armed with attribution data, Daniel made dramatic changes in July:
🔄 Daniel's H2 2024 Strategy Changes
- ELIMINATED Breakout Momentum strategy entirely (negative expectancy)
- DOUBLED allocation to Janus Sweeps (76.8% win rate, 3.9R avg)
- REDUCED VWAP Mean Reversion to occasional setups (low contribution)
- Added Janus confluence filters to improve win rate further
- Increased position size by 30% on highest-conviction Janus setups
| Month | Total P&L | Total Trades | Account Balance | Strategy Mix |
|---|---|---|---|---|
| July | +$8,940 | 28 | $157,340 | 25 Janus, 3 VWAP, 0 Breakouts |
| August | +$7,620 | 31 | $164,960 | 28 Janus, 3 VWAP |
| September | +$6,580 | 25 | $171,540 | 23 Janus, 2 VWAP |
| October | +$9,480 | 27 | $181,020 | 25 Janus, 2 VWAP |
| November | +$8,120 | 24 | $189,140 | 22 Janus, 2 VWAP |
| December | +$460 | 18 | $189,600 | 16 Janus, 2 VWAP (holiday chop) |
| H2 TOTALS | +$41,200 | 153 trades | $189,600 | +27.8% H2 return |
🎯 Before vs. After Attribution Analysis
| Metric | H1 2024 (Flying Blind) | H2 2024 (Attribution-Driven) | Improvement |
|---|---|---|---|
| Total Profit | +$28,400 | +$41,200 | +$12,800 (+45%) |
| Total Trades | 252 | 153 | -99 (-39%) |
| Avg Profit Per Trade | $113 | $269 | +$156 (+138%) |
| Win Rate | 59.5% | 75.2% | +15.7% |
| Average R-Multiple | 1.8R | 3.6R | +1.8R (+100%) |
| Sharpe Ratio | 1.4 | 2.8 | +1.4 (+100%) |
| Period Return | +23.7% | +27.8% | +4.1% |
Result: By eliminating the strategy that was destroying returns and doubling down on his edge, Daniel:
- Made 45% MORE profit (+$41,200 vs +$28,400) in H2
- Took 39% FEWER trades (153 vs 252) - less work!
- Doubled his avg profit per trade ($269 vs $113)
- Increased win rate from 59.5% to 75.2%
- Doubled his Sharpe ratio from 1.4 to 2.8 (excellent risk-adjusted returns)
Full Year 2024 Results
| Period | Profit | Return | Trades | $/Trade | Key Learning |
|---|---|---|---|---|---|
| H1 2024 (No Attribution) | +$28,400 | +23.7% | 252 | $113 | Flying blind, no idea which strategy worked |
| H2 2024 (Post-Attribution) | +$41,200 | +27.8% | 153 | $269 | Killed breakouts, doubled Janus allocation |
| FULL YEAR 2024 | +$69,600 | +58.0% | 405 | $172 | — |
🏆 The Bottom Line: Performance Attribution Changed Everything
Starting Capital: $120,000 (January 1, 2024)
Ending Capital: $189,600 (December 31, 2024)
Total Return: +$69,600 (+58.0% full year)
Attribution-Driven Improvement (H2): +$12,800 extra profit vs. H1 trajectory
What if Daniel had continued H1 approach for full year?
- H1 return: +23.7% ($28,400)
- H2 projected at same rate: +23.7% ($35,156 on higher base)
- Projected full year: +$63,556 (+53.0%)
- Actual result with attribution: +$69,600 (+58.0%)
- Attribution value: +$6,044 extra profit (9.5% boost)
The lesson? Performance attribution reveals hidden truths. Daniel discovered he had a massive edge (+$48,900 from Janus), but was unknowingly sabotaging himself with breakouts (-$23,800). One afternoon of spreadsheet analysis generated $12,800 in extra profit in H2 by eliminating the profit destroyer and doubling down on his edge.
Daniel's advice to other traders: "I thought I knew what was working. I was wrong. Spend 2 hours doing attribution analysis. It might be the most profitable 2 hours of your year."
Your Top 5 Trades Are 80% of Profit
This is the part that hurts (and helps).
Where You Made Money
Top 5 Winners (out of 50 trades):
1. SPY sweep potential reversal: +$2,500 (5R)
2. QQQ potential breakout: +$1,800 (4.5R)
3. IWM mean reversion: +$1,200 (3R)
4. SPY VWAP bounce: +$1,100 (2.8R)
5. QQQ Janus sweep: +$1,000 (2.5R)
Total from top 5: +$7,600 (56% of total profit!)
Lesson: Let. Winners. Run.5 trades = 56% of profit. This is why you hold 3R+ targets.
Where You Lost Money
Bottom 5 Losers:
1. IWM false potential breakout: -$800 (-1R, stop hit)
2. SPY FOMO entry: -$600 (-1R, ignored HTF)
3. QQQ oversize: -$500 (-0.8R, risked 3%)
4. SPY news volatility: -$450 (-1R, VIX spike)
5. IWM late chase: -$400 (-1R, FOMO)
Common themes:
- FOMO/chasing (3/5) ← Fix this!
- Ignored HTF/regime (3/5)
- Oversized (1/5)
Action: Pre-potential entry checklist (HTF, regime, no FOMO)Most losses are preventable. Stop chasing. Stop ignoring context.
🎯 Monthly Review Checklist
- Which strategy contributed most profit?
- Which strategy had best success rate / avg R?
- What's the common theme in losses?
- Did I follow my rules (or break them)?
- What regime performed best? Worst?
Common practice involves monthly. Compound learning, not just capital.
Deep Dive: Which Strategy to Scale, Which to Kill
Janus Sweeps (Your Best Strategy)
Trades: 45
Performance: 71%
Avg R: 3.2R
Total P&L: +$12,500
Best conditions:
- Trending regime: 78% WR
- HTF aligned: 75% WR
- Multi-TF confluence: 82% WR
Worst conditions:
- Ranging regime: 58% WR (skip!)
- VIX > 25: 48% WR (avoid!)
Action: Allocate MORE capital to Janus in trending regimesBreakouts (Marginal Strategy)
Trades: 20
Performance: 55%
Avg R: 1.8R
Total P&L: +$2,400
Analysis: Barely profitable (many false breakouts)
Action: Add volume filter (potential breakout vol > 2x avg)
Or consider killing this strategy entirelyTime-of-Day Performance
9:30-10:30 AM: 12 trades, 50% WR, 1.2R avg (choppy, skip)
10:30 AM-12 PM: 35 trades, 74% WR, 3.5R avg ← BEST window!
12 PM-2 PM: 8 trades, 38% WR, 0.5R avg (lunch chop, avoid)
2 PM-4 PM: 20 trades, 60% WR, 2.3R avg (OK)
Action: Focus 10:30 AM-12 PM, avoid lunch, reduce size 9:30-10:30🎓 Key Takeaways
- Decompose returns: by strategy, asset, time period
- Alpha vs. Beta: separate skill from market exposure
- Top 5 trades = 80% of profit (let winners run!)
- Analyze losses: find patterns, create filters
- Time-of-day matters: avoid low-WR windows
- Review monthly: identify what works, eliminate what doesn't
📝 Practice Exercise
Conduct a Complete Performance Attribution Analysis
- Export your last 3 months of trade data
- Get trade history from your broker or journal
- Include: Date, time, symbol, strategy, potential entry, exit, P&L, R-multiple
- Calculate strategy-level attribution
- Group trades by strategy (Janus sweeps, breakouts, mean reversion, etc.)
- For each strategy: Total P&L, expectancy, avg R, number of trades
- Calculate percentage contribution: (Strategy P&L / Total P&L) × 100
- Identify your top 5 winners and bottom 5 losers
- Sort all trades by P&L (largest gains to largest losses)
- Analyze top 5: What setup? What timeframe? HTF aligned?
- Analyze bottom 5: What went wrong? FOMO? Ignored rules? Bad regime?
- Calculate alpha vs. beta
- Compare your return to SPY return for same period
- Estimate your beta (if your portfolio moves 1.5x SPY, beta = 1.5)
- Calculate alpha: Your return - (Beta × SPY return)
- Create action items
- Which strategy to scale (highest Sharpe ratio)?
- Which strategy to kill (negative or barely positive)?
- What common mistake to fix (based on bottom 5 analysis)?
Goal: Know exactly where your returns came from and make data-driven decisions about what to scale and what to eliminate.
🎮 Test Your Understanding (No Pressure)
Question 1: You made +50% this year. SPY made +20%. Your beta is 2.0. What's your alpha?
Question 2: Your top 5 trades out of 50 total made 60% of your profit. What's the lesson?
If you made it this far, you understand that performance without attribution is noise. Know what works. Double down. Know what doesn't. Eliminate it. That's how you compound an edge.
Related Lessons
Advanced Portfolio Theory
Use Sharpe ratios and Kelly Criterion to optimize allocations.
Read Lesson →Trade Journaling
Systematic trade logging enables performance attribution.
Read Lesson →Trading System Development
Build strategies with measurable edges for clear attribution.
Read Lesson →⏭️ Coming Up Next
Lesson #60: Tax Optimization — Taxes can cost 20-40% of gains. Learn short vs. long-term rates, trader tax status, wash sales, and entity structuring to keep more of what you earn.
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