docs: Complete v9 parameter optimization & backtesting documentation

- v10 removal background (Nov 28, 2025) - v9 baseline performance (05.88, 569 trades, 60.98% WR) - Adaptive leverage implementation (5x high quality, 1x borderline) - Parameter sweep strategy (8 parameters, 65,536 combinations) - EPYC exhaustive sweep status (24 workers, ~17h remaining) - Backtesting infrastructure details - Expected outcomes and analysis plan - Key lessons learned from v10 failure
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+## v9 Parameter Optimization & Backtesting (Nov 28-29, 2025)
+
+**Purpose:** Comprehensive parameter sweep to optimize v9 Money Line indicator for maximum profitability while maintaining quality standards.
+
+**Background - v10 Removal (Nov 28, 2025):**
+- **v10 Status:** FULLY REMOVED - discovered to be "garbage" during initial backtest analysis
+- **v10 Problems Discovered:**
+  1. **Parameter insensitivity:** 72 different configurations produced identical $498.12 P&L
+  2. **Bug in penalty logic:** Price position penalty incorrectly applied to 18.9% position (should only apply to 40-60% chop zone)
+  3. **No edge over v9:** Despite added complexity, no performance improvement
+- **Removal Actions (Nov 28, 2025):**
+  * Removed moneyline_v10_adaptive_position_scoring.pinescript
+  * Removed v10-specific code from backtester modules
+  * Updated all documentation to remove v10 references
+  * Docker rebuild completed successfully
+  * Git commit: 5f77024 "remove: Complete v10 indicator removal - proven garbage"
+- **Lesson:** Parameter insensitivity = no real edge, just noise. Simpler is better.
+
+**v9 Baseline Performance:**
+- **Data:** Nov 2024 - Nov 2025 SOLUSDT 5-minute OHLCV (139,678 rows)
+- **Default Parameters:** flip_threshold=0.6, ma_gap=0.35, momentum_adx=23, long_pos=70, short_pos=25, cooldown_bars=2, momentum_spacing=3, momentum_cooldown=2
+- **Results:** $405.88 PnL, 569 trades, 60.98% WR, 1.022 PF, -$1,360.58 max DD
+- **Baseline established:** Nov 28, 2025
+
+**Adaptive Leverage Implementation (Nov 28, 2025):**
+- **Purpose:** Increase profit potential while maintaining risk management
+- **Configuration:**
+  ```bash
+  USE_ADAPTIVE_LEVERAGE=true
+  HIGH_QUALITY_LEVERAGE=5      # 5x for quality ≥95 LONGs, ≥90 SHORTs
+  LOW_QUALITY_LEVERAGE=1       # 1x for borderline quality (Q90-94 LONGs, Q80-89 SHORTs)
+  QUALITY_LEVERAGE_THRESHOLD=95
+  ```
+- **Rationale:** User chose "option 1" (quality-based adaptive leverage) with 5x instead of 6x for "less risk"
+- **Direction-Specific Thresholds:**
+  * LONGs: Quality ≥95 → 5x, Quality 90-94 → 1x
+  * SHORTs: Quality ≥90 → 5x, Quality 80-89 → 1x
+  * Lower quality than thresholds → blocked by execute endpoint
+- **Expected Impact:** 5× profit on high-quality signals without increasing risk on borderline setups
+- **Status:** ✅ ACTIVE in production (container restarted Nov 28, 2025)
+- **See:** `ADAPTIVE_LEVERAGE_SYSTEM.md` for implementation details
+
+**Parameter Sweep Strategy:**
+- **8 Parameters to Optimize:**
+  1. **flip_threshold:** 0.4, 0.5, 0.6, 0.7 (4 values) - EMA flip confirmation threshold
+  2. **ma_gap:** 0.20, 0.30, 0.40, 0.50 (4 values) - MA50-MA200 convergence bonus
+  3. **momentum_adx:** 18, 21, 24, 27 (4 values) - ADX requirement for momentum filter
+  4. **momentum_long_pos:** 60, 65, 70, 75 (4 values) - Price position for LONG momentum entry
+  5. **momentum_short_pos:** 20, 25, 30, 35 (4 values) - Price position for SHORT momentum entry
+  6. **cooldown_bars:** 1, 2, 3, 4 (4 values) - Bars between signals
+  7. **momentum_spacing:** 2, 3, 4, 5 (4 values) - Bars between momentum confirmations
+  8. **momentum_cooldown:** 1, 2, 3, 4 (4 values) - Momentum-specific cooldown
+- **Total Combinations:** 4^8 = 65,536 exhaustive search
+- **Grid Design:** 4 values per parameter = balanced between granularity and computation time
+
+**Sweep Results - Narrow Grid (27 combinations):**
+- **Date:** Nov 28, 2025 (killed early to port to EPYC)
+- **Top Result:** $496.41 PnL (22% improvement over baseline)
+- **Key Finding:** Parameter insensitivity observed again
+  * Multiple different configurations produced identical results
+  * Suggests v9 edge comes from core EMA logic, not parameter tuning
+  * Similar pattern to v10 (but v9 has proven baseline edge)
+- **Decision:** Proceed with exhaustive 65,536 combo search on EPYC to confirm pattern
+
+**EPYC Server Exhaustive Sweep (Nov 28-29, 2025):**
+- **Hardware:** AMD EPYC 7282 16-Core Processor, Debian 12 Bookworm
+- **Configuration:** 24 workers, 1.60s per combo (4× faster than local 6 workers)
+- **Total Combinations:** 65,536 (full 4^8 grid)
+- **Duration:** ~29 hours estimated
+- **Output:** Top 100 results saved to sweep_v9_exhaustive_epyc.csv
+- **Setup:**
+  * Package: backtest_v9_sweep.tar.gz (1.1MB compressed)
+  * Contents: data/solusdt_5m.csv (1.9MB), backtester modules, sweep scripts
+  * Python env: 3.11.2 with pandas 2.3.3, numpy 2.3.5
+  * Virtual environment: /home/backtest/.venv/
+- **Status:** ✅ RUNNING (started Nov 28, 2025 ~17:00 UTC, ~17h remaining as of Nov 29)
+- **Critical Fixes Applied:**
+  1. Added `source .venv/bin/activate` to run script (fixes ModuleNotFoundError)
+  2. Kept `--top 100` limit (tests all 65,536, saves top 100 to CSV)
+  3. Proper output naming: sweep_v9_exhaustive_epyc.csv
+
+**Backtesting Infrastructure:**
+- **Location:** `/home/icke/traderv4/backtester/` and `/home/backtest/` (EPYC)
+- **Modules:**
+  * `backtester_core.py` - Core backtesting engine with ATR-based TP/SL
+  * `v9_moneyline_ma_gap.py` - v9 indicator logic implementation
+  * `moneyline_core.py` - Shared EMA/signal detection logic
+- **Data:** `data/solusdt_5m.csv` - Nov 2024 to Nov 2025 OHLCV (139,678 5-min bars)
+- **Sweep Script:** `scripts/run_backtest_sweep.py` - Multiprocessing parameter grid search
+  * Progress bar shows hours/minutes (not seconds) for long-running sweeps
+  * Supports --top N to limit output file size
+  * Uses multiprocessing.Pool for parallel execution
+- **Python Environments:**
+  * Local: Python 3.7.3 with .venv (pandas/numpy)
+  * EPYC: Python 3.11.2 with .venv (pandas 2.3.3, numpy 2.3.5)
+- **Setup Scripts:**
+  * `setup_epyc.sh` - Installs python3-venv, creates .venv, installs pandas/numpy
+  * `run_sweep_epyc.sh` - Executes parameter sweep with proper venv activation
+
+**Expected Outcomes:**
+1. **If parameter insensitivity persists:** v9 edge is in core EMA logic, not tuning
+   - Action: Use baseline parameters in production
+   - Conclusion: v9 works because of momentum filter logic, not specific values
+2. **If clear winners emerge:** Optimize production parameters
+   - Action: Update .pinescript with optimal values
+   - Validation: Confirm via forward testing (50-100 trades)
+3. **If quality thresholds need adjustment:**
+   - SHORT threshold 80 may be too strict (could be missing profitable setups)
+   - Analyze win rate distribution around thresholds
+
+**Post-Sweep Analysis Plan:**
+1. Review top 100 results for parameter clustering
+2. Check if top performers share common characteristics
+3. Identify "stability zones" (parameters that consistently perform well)
+4. Compare exhaustive results to baseline ($405.88) and narrow sweep ($496.41)
+5. Make production parameter recommendations
+6. Consider if SHORT quality threshold (80) needs lowering based on blocked signals analysis
+
+**Key Files:**
+- `workflows/trading/moneyline_v9_ma_gap.pinescript` - Production v9 indicator
+- `backtester/v9_moneyline_ma_gap.py` - Python implementation for backtesting
+- `scripts/run_backtest_sweep.py` - Parameter sweep orchestration
+- `run_sweep_epyc.sh` - EPYC execution script (24 workers, venv activation)
+- `ADAPTIVE_LEVERAGE_SYSTEM.md` - Adaptive leverage implementation docs
+- `INDICATOR_V9_MA_GAP_ROADMAP.md` - v9 development roadmap
+
+**Current Production State (Nov 28-29, 2025):**
+- **Indicator:** v9 Money Line with MA Gap + Momentum SHORT Filter
+- **Quality Thresholds:** LONG ≥90, SHORT ≥80
+- **Adaptive Leverage:** ACTIVE (5x high quality, 1x borderline)
+- **Capital:** $540 USDC at 100% health
+- **Expected Profit Boost:** 5× on high-quality signals with adaptive leverage
+- **Backtesting:** Exhaustive parameter sweep in progress (17h remaining)
+
+**Lessons Learned:**
+1. **Parameter insensitivity indicates overfitting:** When many configs give identical results, the edge isn't in parameters
+2. **Simpler is better:** v10 added complexity but no edge → removed completely
+3. **Quality-based leverage scales winners:** 5x on Q95+ signals amplifies edge without increasing borderline risk
+4. **Exhaustive search validates findings:** 65,536 combos confirm if pattern is real or sampling artifact
+5. **Python environments matter:** Always activate venv before running backtests on remote servers
+6. **Portable packages enable distributed computing:** 1.1MB tar.gz enables 16-core EPYC utilization
+
 ## Integration Points

 - **n8n:** Expects exact response format from `/api/trading/execute` (see n8n-complete-workflow.json)