Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

test_deviation.py

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+"""Analyze price deviation from SMA(24) on 1h candles — to calibrate lock threshold."""
+import sys
+sys.path.insert(0, '/root/grid-bot')
+
+from api import TradernetAPI
+from config import (
+    TRADERNET_PUBLIC_KEY, TRADERNET_PRIVATE_KEY, SYMBOL,
+    TRADERNET_BASE_URL, TRADERNET_LOGIN, TRADERNET_PASSWORD,
+)
+from datetime import datetime, timedelta, timezone
+
+api = TradernetAPI(
+    TRADERNET_PUBLIC_KEY, TRADERNET_PRIVATE_KEY,
+    TRADERNET_LOGIN, TRADERNET_PASSWORD, TRADERNET_BASE_URL,
+)
+
+# Fetch 7 days of 1h candles
+dt_to   = datetime.now(tz=timezone.utc)
+dt_from = dt_to - timedelta(days=7)
+raw = api.get_hloc_sync(
+    SYMBOL, 60,
+    dt_from.strftime("%d.%m.%Y %H:%M"),
+    dt_to.strftime("%d.%m.%Y %H:%M"),
+    0,
+)
+hloc   = raw.get("hloc", {}).get(SYMBOL, [])
+ts_lst = raw.get("xSeries", {}).get(SYMBOL, [])
+
+candles = [
+    {"t": int(ts_lst[i]), "c": float(h[3])}
+    for i, h in enumerate(hloc[:len(ts_lst)])
+]
+closes = [c["c"] for c in candles]
+print(f"Got {len(closes)} hourly closes: {closes[0]:.0f} → {closes[-1]:.0f}\n")
+
+# Compute rolling SMA(24) and deviation in %
+sma_p = 24
+devs = []  # (timestamp, price, sma, dev_pct, atr_1h)
+atr_p = 14
+
+# ATR first
+atr_candles = [{"h": float(h[1]), "l": float(h[2]), "c": float(h[3])} for h in hloc[:len(ts_lst)]]
+trs = []
+for i in range(1, len(atr_candles)):
+    h, l, pc = atr_candles[i]["h"], atr_candles[i]["l"], atr_candles[i-1]["c"]
+    tr = max(h-l, abs(h-pc), abs(l-pc))
+    trs.append(tr)
+atr_14 = sum(trs[-atr_p:]) / atr_p if len(trs) >= atr_p else None
+atr_dollar = atr_14
+print(f"ATR(14) on 1h = ${atr_dollar:.2f}\n")
+
+for i in range(sma_p, len(closes)):
+    sma = sum(closes[i-sma_p:i]) / sma_p
+    price = closes[i]
+    ts = candles[i]["t"]
+    dev_pct = (price - sma) / sma * 100
+    devs.append((ts, price, sma, dev_pct))
+
+print(f"{'Time (UTC)':<22} {'Price':>10} {'SMA(24)':>10} {'Dev%':>7} {'Dev$':>8}  ATR-based")
+print("-" * 75)
+for ts, price, sma, dev_pct in devs[-48:]:  # last 48h
+    dt = datetime.fromtimestamp(ts, tz=timezone.utc).strftime("%Y-%m-%d %H:%M")
+    dev_dollar = price - sma
+    dev_atr = abs(dev_dollar) / atr_dollar if atr_dollar else 0
+    lock_1p5 = "🔒" if abs(dev_pct) > 1.5 else "  "
+    lock_2p0 = "🔒" if abs(dev_pct) > 2.0 else "  "
+    lock_3p0 = "🔒" if abs(dev_pct) > 3.0 else "  "
+    print(f"{dt:<22} {price:>10,.0f} {sma:>10,.0f} {dev_pct:>+6.2f}% {dev_dollar:>+8,.0f}  "
+          f"@1.5%{lock_1p5} @2.0%{lock_2p0} @3.0%{lock_3p0}")
+
+print("\n=== Deviation distribution ===")
+pcts = [d[3] for d in devs]
+abs_pcts = [abs(p) for p in pcts]
+import statistics
+print(f"  Mean abs dev: {statistics.mean(abs_pcts):.2f}%")
+print(f"  Median abs dev: {statistics.median(abs_pcts):.2f}%")
+print(f"  Max abs dev: {max(abs_pcts):.2f}%")
+print(f"  % time |dev| < 1%: {sum(1 for p in abs_pcts if p < 1.0)/len(abs_pcts)*100:.1f}%")
+print(f"  % time |dev| < 2%: {sum(1 for p in abs_pcts if p < 2.0)/len(abs_pcts)*100:.1f}%")
+print(f"  % time |dev| < 3%: {sum(1 for p in abs_pcts if p < 3.0)/len(abs_pcts)*100:.1f}%")
+print(f"  % time |dev| > 3%: {sum(1 for p in abs_pcts if p > 3.0)/len(abs_pcts)*100:.1f}%")