Phase 1: Event-driven backtester, 5 strategies, and baseline results

- Built event-driven backtesting engine with spread/slippage modeling,
  3-TP partial closes, trailing stops, and rich trade logging (20+ features)
- Implemented 5 strategy signal generators (MA Breakout, VWAP Reversal,
  Key Level Breakout, EMA Ribbon Scalp, Momentum Exhaustion)
- Full indicator library (EMA, SMA, RSI, ATR, MACD, ADX, Stochastic,
  Session VWAP bands, swing points, key levels, RSI divergence)
- Data pipeline: Dukascopy download, validation, 70/30 train/test split
- Baseline results: all 5 strategies generate 200+ trades on training data
  (Jan 2021 - Aug 2023), best profit factors 0.82-0.96 on select pairs
- Trade logs and reports saved for Phase 3 ML feature engineering

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/backtester.py

@@ -102,7 +102,7 @@ def fetch_candles_from_supabase(instrument, granularity, supabase_client, table)
     """
     print(f"Fetching {instrument} / {granularity} from Supabase...")
 
-    # Supabase JS-style pagination: fetch in pages of 1000
+    # Supabase pagination: fetch in pages of 1000
     all_rows = []
     page_size = 1000
     offset = 0
@@ -113,14 +113,14 @@ def fetch_candles_from_supabase(instrument, granularity, supabase_client, table)
             .eq("instrument", instrument)
             .eq("granularity", granularity)
             .order("time", desc=False)
-            .range(offset, offset + page_size - 1)
+            .range(offset, offset + page_size)
             .execute()
         )
         rows = resp.data or []
         all_rows.extend(rows)
         if len(rows) < page_size:
             break
-        offset += page_size
+        offset += len(rows)
 
     if not all_rows:
         print(f"  No data found for {instrument} / {granularity}.")