liquidity level finder

backend/data/loader.py

@@ -20,8 +20,16 @@ def load_candles(filepath: str) -> list[Candle]:
 
     return candles
 
-if __name__ == "__main__":
+def resample_candles(candles, period=5):
-    candles = load_candles("data.csv")
+    resampled = []
-    print(f"Loaded {len(candles)} candles")
+    for i in range(0, len(candles) - period + 1, period):
-    print(f"First: {candles[0]}")
+        group = candles[i:i + period]
-    print(f"Last: {candles[-1]}")
+        resampled.append(Candle(
+            time_open=group[0].time_open,
+            open=group[0].open,
+            high=max(c.high for c in group),
+            low=min(c.low for c in group),
+            close=group[-1].close,
+            volume=sum(c.volume for c in group)
+        ))
+    return resampled

backend/indicators/liquidity.py

@@ -0,0 +1,46 @@
+def find_liquidity_levels(swings, tolerance=0.015, max_distance=100):
+    levels = []
+    highs = [s for s in swings if s["type"] == "high"]
+    lows = [s for s in swings if s["type"] == "low"]
+
+    used = set()
+    for i, h1 in enumerate(highs):
+        if i in used:
+            continue
+        cluster = [h1]
+        for j, h2 in enumerate(highs):
+            if j != i and j not in used:
+                if abs(h1["price"] - h2["price"]) <= tolerance and abs(h1["index"] - h2["index"]) <= max_distance:
+                    cluster.append(h2)
+                    used.add(j)
+        if len(cluster) >= 2:
+            avg_price = sum(s["price"] for s in cluster) / len(cluster)
+            levels.append({
+                "price": avg_price,
+                "type": "equal_highs",
+                "count": len(cluster),
+                "indexes": [s["index"] for s in cluster]
+            })
+        used.add(i)
+
+    used = set()
+    for i, l1 in enumerate(lows):
+        if i in used:
+            continue
+        cluster = [l1]
+        for j, l2 in enumerate(lows):
+            if j != i and j not in used:
+                if abs(h1["price"] - h2["price"]) <= tolerance and abs(h1["index"] - h2["index"]) <= max_distance:
+                    cluster.append(l2)
+                    used.add(j)
+        if len(cluster) >= 2:
+            avg_price = sum(s["price"] for s in cluster) / len(cluster)
+            levels.append({
+                "price": avg_price,
+                "type": "equal_lows",
+                "count": len(cluster),
+                "indexes": [s["index"] for s in cluster]
+            })
+        used.add(i)
+
+    return levels

backend/run.py

@@ -1,12 +1,23 @@
 from data.loader import load_candles
 from indicators.market_structure import find_swing_points, detect_structure
+from indicators.liquidity import find_liquidity_levels
 
 candles = load_candles("data/data.csv")
 print(f"Loaded {len(candles)} candles")
+from data.loader import load_candles, resample_candles
 
-swings = find_swing_points(candles)
+candles_1m = load_candles("data/data.csv")
-structure = detect_structure(swings)
+candles_3m = resample_candles(candles_1m, period=3)
-print(f"Structure points: {len(structure)}")
+candles_5m = resample_candles(candles_1m, period=5)
+
+print(f"1m: {len(candles_1m)} candles")
+print(f"3m: {len(candles_3m)} candles")
+print(f"5m: {len(candles_5m)} candles")
+
+swings = find_swing_points(candles_5m)
+levels = find_liquidity_levels(swings)
+print(f"Swing points: {len(swings)}")
+print(f"Liquidity levels: {len(levels)}")
+for l in levels[:5]:
+    print(l)
 
-for s in structure[:10]:
-    print(s)