← Назад"""
Parameter Sweep: DCA Z-VWAP (NO Safety Orders)
================================================
Tests combinations of:
- Z Entry: 1.5, 1.8, 2.0, 2.5, 3.0
- TP%: 0.5, 1.0, 1.5, 2.0, 3.0
- SL%: 2, 3, 5, 8
- NATR min: 0.5, 0.75, 1.0
- CHOP min: 0 (off), 40, 45, 50, 55
- Cooldown: 6, 12, 24 bars
Finds top combos by Profit Factor and PnL.
Usage:
python3 backtests/backtest_param_sweep.py
"""
import sys, os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import json
import time
import numpy as np
from datetime import datetime
from itertools import product
from pybit.unified_trading import HTTP
# ============================================================
# FIXED CONFIG
# ============================================================
ORDER_USD = 7.0
LEVERAGE = 3
VWAP_PERIOD = 50
MAKER_FEE = 0.0002
TAKER_FEE = 0.00055
TIMEFRAME = "5"
DAYS = 7
MIN_VOLUME_24H = 20_000_000
MAX_DEALS_CONCURRENT = 6
BLACKLIST = {"BTCUSDT", "ETHUSDT", "TRXUSDT", "USDCUSDT", "BTCPERP", "ETHPERP"}
# ============================================================
# PARAMETER GRID
# ============================================================
PARAM_GRID = {
"z_entry": [1.5, 1.8, 2.0, 2.5, 3.0],
"tp_pct": [0.5, 1.0, 1.5, 2.0, 3.0],
"sl_pct": [2.0, 3.0, 5.0, 8.0],
"natr_min": [0.5, 0.75, 1.0],
"chop_min": [0, 40, 50, 55],
"cooldown": [6, 12],
}
session = HTTP(testnet=False)
# ============================================================
# DATA
# ============================================================
def get_symbols():
resp = session.get_tickers(category="linear")
if resp["retCode"] != 0:
return []
symbols = []
for t in resp["result"]["list"]:
sym = t["symbol"]
if not sym.endswith("USDT") or sym in BLACKLIST:
continue
vol = float(t.get("turnover24h", 0))
if vol >= MIN_VOLUME_24H:
symbols.append({"symbol": sym, "volume_24h": vol})
symbols.sort(key=lambda x: x["volume_24h"], reverse=True)
return symbols[:50]
def fetch_klines(symbol, interval, days=7):
all_klines = []
bars_needed = days * 24 * 60 // int(interval)
end_time = int(datetime.now().timestamp() * 1000)
while len(all_klines) < bars_needed:
try:
resp = session.get_kline(category="linear", symbol=symbol,
interval=interval, limit=1000, end=end_time)
if resp["retCode"] != 0:
break
items = resp["result"]["list"]
if not items:
break
for item in items:
all_klines.append({
"ts": int(item[0]),
"o": float(item[1]), "h": float(item[2]),
"l": float(item[3]), "c": float(item[4]),
"v": float(item[5]),
})
end_time = int(items[-1][0]) - 1
if len(items) < 1000:
break
except:
break
all_klines.reverse()
seen = set()
unique = []
for k in all_klines:
if k["ts"] not in seen:
seen.add(k["ts"])
unique.append(k)
return unique[-bars_needed:] if len(unique) > bars_needed else unique
# ============================================================
# INDICATORS (vectorized)
# ============================================================
def calc_all_indicators(closes, highs, lows, volumes):
n = len(closes)
# Z-VWAP
z_scores = np.zeros(n)
for i in range(VWAP_PERIOD, n):
h = highs[i-VWAP_PERIOD:i]
l = lows[i-VWAP_PERIOD:i]
c = closes[i-VWAP_PERIOD:i]
v = volumes[i-VWAP_PERIOD:i]
tp = (h + l + c) / 3
ctv = np.cumsum(tp * v)
cv = np.cumsum(v)
cv_safe = np.where(cv == 0, 1, cv)
vwap_arr = ctv / cv_safe
vwap = vwap_arr[-1]
dev = c - vwap_arr
std = np.std(dev)
if std > 0:
z_scores[i] = (closes[i] - vwap) / std
# NATR (rolling 14)
natr = np.zeros(n)
for i in range(14, n):
trs = []
for j in range(i-13, i+1):
tr = max(highs[j] - lows[j],
abs(highs[j] - closes[j-1]),
abs(lows[j] - closes[j-1]))
trs.append(tr)
atr = np.mean(trs)
natr[i] = (atr / closes[i]) * 100 if closes[i] > 0 else 0
# CHOP Index (14)
chop = np.full(n, 50.0)
for i in range(14, n):
atr_sum = 0
for j in range(i-13, i+1):
tr = max(highs[j] - lows[j],
abs(highs[j] - closes[j-1]),
abs(lows[j] - closes[j-1]))
atr_sum += tr
hi = np.max(highs[i-13:i+1])
lo = np.min(lows[i-13:i+1])
rng = hi - lo
if rng > 0:
chop[i] = 100 * np.log10(atr_sum / rng) / np.log10(14)
return z_scores, natr, chop
# ============================================================
# SIMPLE DEAL (no SO)
# ============================================================
def simulate_deals(z_scores, natr, chop, closes, highs, lows, timestamps, params):
n = len(closes)
z_entry = params["z_entry"]
tp_pct = params["tp_pct"]
sl_pct = params["sl_pct"]
natr_min = params["natr_min"]
chop_min = params["chop_min"]
cooldown = params["cooldown"]
deals = []
in_trade = False
side = None
entry_price = 0
entry_bar = 0
cooldown_until = 0
for i in range(VWAP_PERIOD, n):
if in_trade:
# Check exits
z = z_scores[i]
closed = False
close_price = 0
reason = ""
if side == "LONG":
tp_price = entry_price * (1 + tp_pct / 100)
sl_price = entry_price * (1 - sl_pct / 100)
# SL first
if lows[i] <= sl_price:
close_price = sl_price
reason = "SL"
closed = True
elif highs[i] >= tp_price:
close_price = tp_price
reason = "TP"
closed = True
elif z >= -0.3 and closes[i] > entry_price:
close_price = closes[i]
reason = "Z-TP"
closed = True
else: # SHORT
tp_price = entry_price * (1 - tp_pct / 100)
sl_price = entry_price * (1 + sl_pct / 100)
if highs[i] >= sl_price:
close_price = sl_price
reason = "SL"
closed = True
elif lows[i] <= tp_price:
close_price = tp_price
reason = "TP"
closed = True
elif z <= 0.3 and closes[i] < entry_price:
close_price = closes[i]
reason = "Z-TP"
closed = True
# Time stop: 36 bars (3h)
if not closed and (i - entry_bar) >= 36:
close_price = closes[i]
reason = "TIME"
closed = True
if closed:
qty = (ORDER_USD * LEVERAGE) / entry_price
if side == "LONG":
pnl = qty * (close_price - entry_price)
else:
pnl = qty * (entry_price - close_price)
fees = qty * entry_price * TAKER_FEE + qty * close_price * TAKER_FEE
pnl -= fees
deals.append({
"side": side,
"pnl": pnl,
"reason": reason,
"bars": i - entry_bar,
})
in_trade = False
cooldown_until = i + cooldown
continue
# Check entry
if i < cooldown_until:
continue
z = z_scores[i]
if abs(z) <= z_entry:
continue
# NATR filter
if natr[i] < natr_min:
continue
# CHOP filter
if chop_min > 0 and chop[i] < chop_min:
continue
side = "LONG" if z < -z_entry else "SHORT"
entry_price = closes[i]
entry_bar = i
in_trade = True
# Force close open
if in_trade:
qty = (ORDER_USD * LEVERAGE) / entry_price
cp = closes[-1]
if side == "LONG":
pnl = qty * (cp - entry_price)
else:
pnl = qty * (entry_price - cp)
fees = qty * entry_price * TAKER_FEE + qty * cp * TAKER_FEE
pnl -= fees
deals.append({"side": side, "pnl": pnl, "reason": "END", "bars": n - 1 - entry_bar})
return deals
# ============================================================
# MAIN
# ============================================================
def main():
print("=" * 70)
print(" PARAMETER SWEEP — Z-VWAP NO SO")
print(" Finding optimal params for live")
print("=" * 70)
# Fetch symbols
print("\n Fetching symbols...")
symbols_data = get_symbols()
print(f" {len(symbols_data)} symbols with vol >= ${MIN_VOLUME_24H/1e6:.0f}M")
# Fetch all kline data
print("\n Downloading klines...")
all_data = {}
for idx, sd in enumerate(symbols_data):
sym = sd["symbol"]
print(f" [{idx+1}/{len(symbols_data)}] {sym}...", end=" ", flush=True)
time.sleep(0.12)
klines = fetch_klines(sym, TIMEFRAME, days=DAYS)
if len(klines) < VWAP_PERIOD + 50:
print("skip (few bars)")
continue
closes = np.array([k["c"] for k in klines])
highs = np.array([k["h"] for k in klines])
lows = np.array([k["l"] for k in klines])
volumes = np.array([k["v"] for k in klines])
timestamps = [k["ts"] for k in klines]
z_scores, natr, chop = calc_all_indicators(closes, highs, lows, volumes)
all_data[sym] = {
"closes": closes, "highs": highs, "lows": lows,
"z": z_scores, "natr": natr, "chop": chop, "ts": timestamps,
"vol": sd["volume_24h"],
}
print(f"OK ({len(klines)} bars)")
print(f"\n {len(all_data)} symbols loaded")
# Generate param combos
keys = list(PARAM_GRID.keys())
values = list(PARAM_GRID.values())
combos = list(product(*values))
print(f" {len(combos)} parameter combinations to test\n")
results = []
for ci, combo in enumerate(combos):
params = dict(zip(keys, combo))
all_deals = []
for sym, data in all_data.items():
deals = simulate_deals(
data["z"], data["natr"], data["chop"],
data["closes"], data["highs"], data["lows"],
data["ts"], params
)
for d in deals:
d["symbol"] = sym
all_deals.extend(deals)
if len(all_deals) < 5:
continue
total_pnl = sum(d["pnl"] for d in all_deals)
wins = [d for d in all_deals if d["pnl"] > 0]
losses = [d for d in all_deals if d["pnl"] <= 0]
wr = len(wins) / len(all_deals) * 100
gp = sum(d["pnl"] for d in wins) if wins else 0
gl = abs(sum(d["pnl"] for d in losses)) if losses else 0.001
pf = gp / gl if gl > 0 else 999
avg_bars = sum(d["bars"] for d in all_deals) / len(all_deals)
# Close reason breakdown
tp_cnt = sum(1 for d in all_deals if d["reason"] == "TP")
ztp_cnt = sum(1 for d in all_deals if d["reason"] == "Z-TP")
sl_cnt = sum(1 for d in all_deals if d["reason"] == "SL")
time_cnt = sum(1 for d in all_deals if d["reason"] == "TIME")
results.append({
**params,
"deals": len(all_deals),
"pnl": round(total_pnl, 2),
"wr": round(wr, 1),
"pf": round(pf, 2),
"avg_win": round(gp / len(wins), 3) if wins else 0,
"avg_loss": round(gl / len(losses), 3) if losses else 0,
"avg_bars": round(avg_bars, 1),
"tp": tp_cnt,
"ztp": ztp_cnt,
"sl": sl_cnt,
"time": time_cnt,
})
if (ci + 1) % 100 == 0:
print(f" ... {ci+1}/{len(combos)} combos tested")
print(f"\n Done! {len(results)} valid combos (>= 5 deals)")
# Sort by PF then PnL
results.sort(key=lambda x: (x["pf"], x["pnl"]), reverse=True)
# TOP 20 by PF (min 10 deals)
print(f"\n{'='*100}")
print(f" TOP 20 by Profit Factor (min 10 deals)")
print(f"{'='*100}")
header = f"{'#':>3} {'Z':>4} {'TP%':>5} {'SL%':>5} {'NATR':>5} {'CHOP':>4} {'CD':>3} | {'Deals':>5} {'PnL':>8} {'WR%':>5} {'PF':>6} {'AvgW':>7} {'AvgL':>7} {'Bars':>5} | {'TP':>4} {'ZTP':>4} {'SL':>4} {'TIM':>4}"
print(header)
print("-" * 100)
shown = 0
for r in results:
if r["deals"] < 10:
continue
shown += 1
if shown > 20:
break
emoji = "+" if r["pnl"] > 0 else "-"
print(f"{shown:>3} {r['z_entry']:>4.1f} {r['tp_pct']:>5.1f} {r['sl_pct']:>5.1f} {r['natr_min']:>5.2f} {r['chop_min']:>4.0f} {r['cooldown']:>3} | {r['deals']:>5} ${r['pnl']:>+7.2f} {r['wr']:>5.1f} {r['pf']:>6.2f} ${r['avg_win']:>6.3f} ${r['avg_loss']:>6.3f} {r['avg_bars']:>5.1f} | {r['tp']:>4} {r['ztp']:>4} {r['sl']:>4} {r['time']:>4}")
# TOP 20 by PnL (min 10 deals)
results_pnl = sorted(results, key=lambda x: x["pnl"], reverse=True)
print(f"\n{'='*100}")
print(f" TOP 20 by Total PnL (min 10 deals)")
print(f"{'='*100}")
print(header)
print("-" * 100)
shown = 0
for r in results_pnl:
if r["deals"] < 10:
continue
shown += 1
if shown > 20:
break
print(f"{shown:>3} {r['z_entry']:>4.1f} {r['tp_pct']:>5.1f} {r['sl_pct']:>5.1f} {r['natr_min']:>5.2f} {r['chop_min']:>4.0f} {r['cooldown']:>3} | {r['deals']:>5} ${r['pnl']:>+7.2f} {r['wr']:>5.1f} {r['pf']:>6.2f} ${r['avg_win']:>6.3f} ${r['avg_loss']:>6.3f} {r['avg_bars']:>5.1f} | {r['tp']:>4} {r['ztp']:>4} {r['sl']:>4} {r['time']:>4}")
# TOP 20 by WR (min 10 deals, PF > 1)
results_wr = sorted([r for r in results if r["pf"] > 1.0], key=lambda x: x["wr"], reverse=True)
print(f"\n{'='*100}")
print(f" TOP 20 by Win Rate (min 10 deals, PF > 1)")
print(f"{'='*100}")
print(header)
print("-" * 100)
shown = 0
for r in results_wr:
if r["deals"] < 10:
continue
shown += 1
if shown > 20:
break
print(f"{shown:>3} {r['z_entry']:>4.1f} {r['tp_pct']:>5.1f} {r['sl_pct']:>5.1f} {r['natr_min']:>5.2f} {r['chop_min']:>4.0f} {r['cooldown']:>3} | {r['deals']:>5} ${r['pnl']:>+7.2f} {r['wr']:>5.1f} {r['pf']:>6.2f} ${r['avg_win']:>6.3f} ${r['avg_loss']:>6.3f} {r['avg_bars']:>5.1f} | {r['tp']:>4} {r['ztp']:>4} {r['sl']:>4} {r['time']:>4}")
# BALANCED PICK (PF > 1, >= 15 deals, best PnL)
balanced = [r for r in results if r["pf"] > 1.0 and r["deals"] >= 15]
balanced.sort(key=lambda x: x["pnl"], reverse=True)
if balanced:
print(f"\n{'='*100}")
print(f" RECOMMENDED (PF > 1, >= 15 deals, sorted by PnL)")
print(f"{'='*100}")
print(header)
print("-" * 100)
for i, r in enumerate(balanced[:10]):
print(f"{i+1:>3} {r['z_entry']:>4.1f} {r['tp_pct']:>5.1f} {r['sl_pct']:>5.1f} {r['natr_min']:>5.2f} {r['chop_min']:>4.0f} {r['cooldown']:>3} | {r['deals']:>5} ${r['pnl']:>+7.2f} {r['wr']:>5.1f} {r['pf']:>6.2f} ${r['avg_win']:>6.3f} ${r['avg_loss']:>6.3f} {r['avg_bars']:>5.1f} | {r['tp']:>4} {r['ztp']:>4} {r['sl']:>4} {r['time']:>4}")
# Save
output_path = os.path.join(os.path.dirname(__file__), "results_param_sweep.json")
with open(output_path, "w") as f:
json.dump({"grid": PARAM_GRID, "results": results[:100]}, f, indent=2)
print(f"\n Saved top 100 to {output_path}")
if __name__ == "__main__":
main()