← Назад"""
Adaptive Grid Spacing Backtest — spacing tied to NATR
Идея: spacing = NATR(5m) × multiplier
Если монета волатильная (NATR 2%) → spacing шире (0.6%)
Если спокойная (NATR 0.5%) → spacing уже (0.15%)
Тестируем:
A) Fixed 0.1% (текущий baseline)
B) Fixed 0.2%, 0.3% (простые варианты)
C) Adaptive: spacing = NATR_5m × 0.3
D) Adaptive: spacing = NATR_5m × 0.5
E) Adaptive: spacing = NATR_5m × 0.7
+ Sideways filter (score >= 50 на 15m) vs без фильтра
Usage: python3 backtest_adaptive_spacing.py
"""
import requests
import pandas as pd
import numpy as np
import time
import json
from datetime import datetime, timedelta
from pathlib import Path
# ============================================================
# CONFIG
# ============================================================
SYMBOLS = [
"ETHUSDT", "DOGEUSDT", "PENGUUSDT", "ENAUSDT",
"NEARUSDT", "WLDUSDT", "SOLUSDT", "ARBUSDT",
"XRPUSDT", "LINKUSDT", "SUIUSDT", "OPUSDT",
"ADAUSDT", "UNIUSDT", "AVAXUSDT",
]
DAYS_BACK = 14
DEPOSIT = 50.0
LEVERAGE = 10
POSITION_SIZE_USD = 3.0
FEE_PCT = 0.02 / 100 # maker 0.02%
GRID_LEVELS = 8
MAX_LOSS_PCT = 3.0
# Spacing variants to test
SPACING_CONFIGS = [
{'name': 'fixed_0.1%', 'type': 'fixed', 'value': 0.1},
{'name': 'fixed_0.2%', 'type': 'fixed', 'value': 0.2},
{'name': 'fixed_0.3%', 'type': 'fixed', 'value': 0.3},
{'name': 'natr×0.3', 'type': 'adaptive', 'mult': 0.3, 'min': 0.08, 'max': 0.8},
{'name': 'natr×0.5', 'type': 'adaptive', 'mult': 0.5, 'min': 0.10, 'max': 1.0},
{'name': 'natr×0.7', 'type': 'adaptive', 'mult': 0.7, 'min': 0.12, 'max': 1.2},
]
# Screener params (15m)
SCREENER_TF = '15m'
BB_PERIOD = 20
BB_STD = 2.0
ADX_PERIOD = 14
RANGE_LOOKBACK = 24
SCORE_THRESHOLD = 45 # slightly lower to get more samples
# Session = 1 hour of 1m candles
SESSION_CANDLES = 60
SESSION_COOLDOWN = 5 # candles gap between sessions
# NATR calc on 5m resampled from 1m
NATR_PERIOD = 14
# ============================================================
# DATA FETCH
# ============================================================
def fetch_klines(symbol, interval, days_back):
url = "https://fapi.binance.com/fapi/v1/klines"
end_ts = int(time.time() * 1000)
start_ts = int((time.time() - days_back * 86400) * 1000)
all_candles = []
current_start = start_ts
while current_start < end_ts:
params = {"symbol": symbol, "interval": interval,
"startTime": current_start, "limit": 1500}
try:
resp = requests.get(url, params=params, timeout=10)
data = resp.json()
if not isinstance(data, list) or len(data) == 0:
break
all_candles.extend(data)
current_start = data[-1][0] + 1
time.sleep(0.08)
except Exception as e:
print(f" [fetch] Error: {e}")
time.sleep(1)
continue
df = pd.DataFrame(all_candles, columns=[
'timestamp', 'open', 'high', 'low', 'close', 'volume',
'close_time', 'quote_volume', 'trades', 'taker_buy_base',
'taker_buy_quote', 'ignore'
])
for col in ['open', 'high', 'low', 'close', 'volume', 'quote_volume']:
df[col] = df[col].astype(float)
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
df = df.drop_duplicates(subset='timestamp').sort_values('timestamp').reset_index(drop=True)
return df
# ============================================================
# RESAMPLE 1m → 5m for NATR
# ============================================================
def calc_natr_5m(df_1m):
"""Resample 1m to 5m and calc NATR(14) = ATR/close * 100"""
df = df_1m.set_index('timestamp').resample('5min').agg({
'open': 'first', 'high': 'max', 'low': 'min',
'close': 'last', 'volume': 'sum'
}).dropna().reset_index()
high, low, close = df['high'], df['low'], df['close']
tr1 = high - low
tr2 = (high - close.shift(1)).abs()
tr3 = (low - close.shift(1)).abs()
tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
atr = tr.ewm(alpha=1/NATR_PERIOD, min_periods=NATR_PERIOD).mean()
df['natr_5m'] = (atr / close) * 100
return df[['timestamp', 'natr_5m']].dropna()
# ============================================================
# SCREENER INDICATORS (15m)
# ============================================================
def calc_screener(df):
df['bb_mid'] = df['close'].rolling(BB_PERIOD).mean()
df['bb_std'] = df['close'].rolling(BB_PERIOD).std()
df['bb_upper'] = df['bb_mid'] + BB_STD * df['bb_std']
df['bb_lower'] = df['bb_mid'] - BB_STD * df['bb_std']
df['bb_width'] = ((df['bb_upper'] - df['bb_lower']) / df['bb_mid']) * 100
high, low, close = df['high'], df['low'], df['close']
plus_dm = high.diff()
minus_dm = -low.diff()
plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
tr1 = high - low
tr2 = (high - close.shift(1)).abs()
tr3 = (low - close.shift(1)).abs()
tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
atr = tr.ewm(alpha=1/ADX_PERIOD, min_periods=ADX_PERIOD).mean()
plus_di = 100 * (plus_dm.ewm(alpha=1/ADX_PERIOD, min_periods=ADX_PERIOD).mean() / atr)
minus_di = 100 * (minus_dm.ewm(alpha=1/ADX_PERIOD, min_periods=ADX_PERIOD).mean() / atr)
dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di + 1e-10)
df['adx'] = dx.ewm(alpha=1/ADX_PERIOD, min_periods=ADX_PERIOD).mean()
df['natr'] = (atr / close) * 100
df['range_high'] = df['high'].rolling(RANGE_LOOKBACK).max()
df['range_low'] = df['low'].rolling(RANGE_LOOKBACK).min()
rng = df['range_high'] - df['range_low']
df['range_pos'] = (df['close'] - df['range_low']) / rng.replace(0, np.nan)
df['candle_dir'] = np.where(df['close'] > df['open'], 1, -1)
df['dir_change'] = (df['candle_dir'] != df['candle_dir'].shift(1)).astype(int)
df['dir_changes'] = df['dir_change'].rolling(RANGE_LOOKBACK).sum()
return df
def sideways_score(row):
score = 0
adx = row.get('adx', np.nan)
bb_w = row.get('bb_width', np.nan)
rp = row.get('range_pos', np.nan)
dc = row.get('dir_changes', np.nan)
natr = row.get('natr', np.nan)
if any(pd.isna(x) for x in [adx, bb_w, rp, dc, natr]):
return 0
# ADX → 25 pts
if adx <= 5:
score += 25
elif adx <= 20:
score += 25 * (1 - (adx - 5) / 15)
elif adx <= 30:
score += max(0, -5 * (adx - 20) / 10)
else:
score -= 10
# BB width 1.5-4% → 20 pts
if 1.5 <= bb_w <= 4.0:
if bb_w <= 2.5:
score += 20 * (bb_w - 1.5) / 1.0
else:
score += 20 * (4.0 - bb_w) / 1.5
elif 0.5 <= bb_w < 1.5:
score += 5
# Range pos 0.3-0.7 → 20 pts
if 0.3 <= rp <= 0.7:
score += 20 * (1 - abs(rp - 0.5) / 0.2)
elif 0.2 <= rp < 0.3 or 0.7 < rp <= 0.8:
score += 5
# Direction changes → 20 pts
max_ch = RANGE_LOOKBACK * 0.7
if dc >= 8:
score += min(20, 20 * (dc - 8) / (max_ch - 8))
# NATR 0.15-0.6% → 15 pts
if 0.15 <= natr <= 0.6:
if natr <= 0.3:
score += 15 * (natr - 0.15) / 0.15
else:
score += 15 * (0.6 - natr) / 0.3
elif 0.1 <= natr < 0.15:
score += 3
return max(0, round(score, 1))
# ============================================================
# GRID ENGINE with variable spacing
# ============================================================
def run_grid_session(df_1m, start_idx, spacing_pct, duration=SESSION_CANDLES):
"""Grid session with given spacing %"""
if start_idx + 5 >= len(df_1m):
return None
end_idx = min(start_idx + duration, len(df_1m) - 1)
mid_price = df_1m['close'].iloc[start_idx]
buy_levels = [mid_price * (1 - lvl * spacing_pct / 100) for lvl in range(1, GRID_LEVELS + 1)]
sell_levels = [mid_price * (1 + lvl * spacing_pct / 100) for lvl in range(1, GRID_LEVELS + 1)]
buy_fills = [False] * GRID_LEVELS
sell_fills = [False] * GRID_LEVELS
positions = []
pnl = 0.0
fees = 0.0
trades = 0
round_trips = 0
max_capital = GRID_LEVELS * 2 * POSITION_SIZE_USD
max_loss = max_capital * MAX_LOSS_PCT / 100
close_reason = 'timeout'
for j in range(start_idx + 1, end_idx):
price = df_1m['close'].iloc[j]
lo = df_1m['low'].iloc[j]
hi = df_1m['high'].iloc[j]
for lvl in range(GRID_LEVELS):
if not buy_fills[lvl] and lo <= buy_levels[lvl]:
buy_fills[lvl] = True
positions.append(('long', buy_levels[lvl]))
fees += POSITION_SIZE_USD * LEVERAGE * FEE_PCT
trades += 1
for lvl in range(GRID_LEVELS):
if not sell_fills[lvl] and hi >= sell_levels[lvl]:
sell_fills[lvl] = True
positions.append(('short', sell_levels[lvl]))
fees += POSITION_SIZE_USD * LEVERAGE * FEE_PCT
trades += 1
# Round-trips
for lvl in range(GRID_LEVELS):
if buy_fills[lvl] and sell_fills[lvl]:
spread = sell_levels[lvl] - buy_levels[lvl]
qty = (POSITION_SIZE_USD * LEVERAGE) / buy_levels[lvl]
pnl += qty * spread
fees += POSITION_SIZE_USD * LEVERAGE * FEE_PCT * 2
buy_fills[lvl] = False
sell_fills[lvl] = False
round_trips += 1
positions = [p for p in positions if not (p[0] == 'long' and abs(p[1] - buy_levels[lvl]) < 1e-10)]
positions = [p for p in positions if not (p[0] == 'short' and abs(p[1] - sell_levels[lvl]) < 1e-10)]
# Unrealized check
unrealized = 0
for side, entry_px in positions:
qty = (POSITION_SIZE_USD * LEVERAGE) / entry_px
if side == 'long':
unrealized += qty * (price - entry_px)
else:
unrealized += qty * (entry_px - price)
if pnl + unrealized - fees < -max_loss and positions:
for side, entry_px in positions:
qty = (POSITION_SIZE_USD * LEVERAGE) / entry_px
if side == 'long':
pnl += qty * (price - entry_px)
else:
pnl += qty * (entry_px - price)
fees += POSITION_SIZE_USD * LEVERAGE * FEE_PCT
positions = []
close_reason = 'max_loss'
break
# Close remaining
if positions:
price = df_1m['close'].iloc[min(end_idx, len(df_1m) - 1)]
for side, entry_px in positions:
qty = (POSITION_SIZE_USD * LEVERAGE) / entry_px
if side == 'long':
pnl += qty * (price - entry_px)
else:
pnl += qty * (entry_px - price)
fees += POSITION_SIZE_USD * LEVERAGE * FEE_PCT
net = pnl - fees
if trades == 0:
return None
return {
'pnl': round(net, 4),
'gross_pnl': round(pnl, 4),
'trades': trades,
'round_trips': round_trips,
'fees': round(fees, 4),
'close_reason': close_reason,
'spacing': round(spacing_pct, 3),
}
# ============================================================
# MAIN
# ============================================================
if __name__ == "__main__":
print("=" * 70)
print(" ADAPTIVE SPACING BACKTEST — NATR-based vs Fixed")
print(f" {len(SYMBOLS)} coins | {DAYS_BACK}d | ${DEPOSIT} dep | {LEVERAGE}x")
print(f" Grid: {GRID_LEVELS} lvl × ${POSITION_SIZE_USD} | Session: {SESSION_CANDLES}min")
print("=" * 70)
# Results per spacing config
all_results = {cfg['name']: {'sessions': [], 'filtered': []} for cfg in SPACING_CONFIGS}
natr_stats = []
for sym in SYMBOLS:
print(f"\n 📊 {sym}...")
# Fetch 1m
df_1m = fetch_klines(sym, '1m', DAYS_BACK)
if len(df_1m) < 1000:
print(f" skip ({len(df_1m)} candles)")
continue
# Calc NATR on 5m
df_natr5 = calc_natr_5m(df_1m)
if len(df_natr5) < 50:
print(f" skip (not enough 5m data)")
continue
# Fetch 15m for screener
df_15m = fetch_klines(sym, SCREENER_TF, DAYS_BACK)
df_15m = calc_screener(df_15m)
df_15m['sw_score'] = df_15m.apply(sideways_score, axis=1)
# Build lookup: timestamp → natr_5m (nearest)
natr_lookup = df_natr5.set_index('timestamp')['natr_5m']
# Process each hour window
# Step through 1m data in SESSION_CANDLES chunks
warmup_1m = 100 # skip first 100 candles
i = warmup_1m
sym_count = 0
while i + SESSION_CANDLES < len(df_1m):
ts = df_1m['timestamp'].iloc[i]
# Get current NATR from 5m (closest timestamp <=)
natr_mask = natr_lookup.index <= ts
if natr_mask.sum() == 0:
i += SESSION_CANDLES + SESSION_COOLDOWN
continue
current_natr = natr_lookup.loc[natr_mask].iloc[-1]
if pd.isna(current_natr) or current_natr <= 0:
i += SESSION_CANDLES + SESSION_COOLDOWN
continue
# Get screener score (closest 15m candle <=)
scr_mask = df_15m['timestamp'] <= ts
if scr_mask.sum() == 0:
i += SESSION_CANDLES + SESSION_COOLDOWN
continue
scr_row = df_15m.loc[scr_mask].iloc[-1]
sw_score = scr_row['sw_score']
natr_stats.append(current_natr)
# Run grid for each spacing config
for cfg in SPACING_CONFIGS:
if cfg['type'] == 'fixed':
spacing = cfg['value']
else:
spacing = current_natr * cfg['mult']
spacing = max(cfg['min'], min(cfg['max'], spacing))
result = run_grid_session(df_1m, i, spacing)
if result:
result['symbol'] = sym
result['natr_5m'] = round(current_natr, 3)
result['sw_score'] = round(sw_score, 1)
result['ts'] = str(ts)
all_results[cfg['name']]['sessions'].append(result)
if sw_score >= SCORE_THRESHOLD:
all_results[cfg['name']]['filtered'].append(result)
sym_count += 1
i += SESSION_CANDLES + SESSION_COOLDOWN
print(f" {sym_count} windows processed | avg NATR_5m: {np.mean(natr_stats[-sym_count:]) if sym_count else 0:.3f}%")
# ============================================================
# REPORTS
# ============================================================
print("\n" + "=" * 70)
print(" 🏆 ИТОГО ПО ВСЕМ МОНЕТАМ")
print("=" * 70)
# NATR distribution
if natr_stats:
print(f"\n NATR(5m) distribution across all windows:")
for pct in [10, 25, 50, 75, 90]:
print(f" P{pct}: {np.percentile(natr_stats, pct):.3f}%")
# Summary table
print(f"\n {'Config':<15} {'Mode':<10} {'Sess':>6} {'WR':>6} {'PnL':>10} {'Avg PnL':>10} {'RTs':>6} {'Avg Spc':>8} {'MaxLoss':>8}")
print(f" {'─'*79}")
summary_data = {}
for cfg in SPACING_CONFIGS:
name = cfg['name']
for mode, label in [('sessions', 'ALL'), ('filtered', 'FILT')]:
sess = all_results[name][mode]
if not sess:
continue
total_pnl = sum(s['pnl'] for s in sess)
wins = len([s for s in sess if s['pnl'] > 0])
wr = 100 * wins / len(sess)
avg_pnl = total_pnl / len(sess)
rts = sum(s['round_trips'] for s in sess)
avg_spc = np.mean([s['spacing'] for s in sess])
ml = len([s for s in sess if s['close_reason'] == 'max_loss'])
print(f" {name:<15} {label:<10} {len(sess):>6} {wr:>5.0f}% ${total_pnl:>8.2f} ${avg_pnl:>8.4f} {rts:>6} {avg_spc:>7.3f}% {ml:>8}")
summary_data[f"{name}_{mode}"] = {
'sessions': len(sess),
'win_rate': round(wr, 1),
'total_pnl': round(total_pnl, 4),
'avg_pnl': round(avg_pnl, 4),
'round_trips': rts,
'avg_spacing': round(avg_spc, 3),
'max_loss_stops': ml,
}
# NATR bracket analysis — which NATR ranges are profitable?
print(f"\n 📊 PnL by NATR bracket (best adaptive config):")
# Find best adaptive config
best_adaptive = None
best_pnl = -9999
for cfg in SPACING_CONFIGS:
if cfg['type'] == 'adaptive':
sess = all_results[cfg['name']]['sessions']
if sess:
tp = sum(s['pnl'] for s in sess)
if tp > best_pnl:
best_pnl = tp
best_adaptive = cfg['name']
if best_adaptive:
sess = all_results[best_adaptive]['sessions']
brackets = [(0, 0.3), (0.3, 0.5), (0.5, 0.8), (0.8, 1.2), (1.2, 2.0), (2.0, 99)]
print(f"\n Config: {best_adaptive}")
print(f" {'NATR range':<15} {'Sess':>6} {'WR':>6} {'PnL':>10} {'Avg PnL':>10} {'Avg Spc':>8} {'RTs':>6}")
print(f" {'─'*61}")
for lo, hi in brackets:
subset = [s for s in sess if lo <= s['natr_5m'] < hi]
if not subset:
continue
tp = sum(s['pnl'] for s in subset)
w = len([s for s in subset if s['pnl'] > 0])
wr = 100 * w / len(subset)
avg = tp / len(subset)
avg_s = np.mean([s['spacing'] for s in subset])
rt = sum(s['round_trips'] for s in subset)
print(f" {lo:.1f}-{hi:.1f}%{'':<9} {len(subset):>6} {wr:>5.0f}% ${tp:>8.2f} ${avg:>8.4f} {avg_s:>7.3f}% {rt:>6}")
# Score bracket analysis for best config
print(f"\n 📊 PnL by Sideways Score (best adaptive: {best_adaptive}):")
if best_adaptive:
sess = all_results[best_adaptive]['sessions']
brackets = [(0, 20), (20, 35), (35, 50), (50, 65), (65, 100)]
print(f" {'Score':<15} {'Sess':>6} {'WR':>6} {'PnL':>10} {'Avg PnL':>10} {'RTs':>6}")
print(f" {'─'*53}")
for lo, hi in brackets:
subset = [s for s in sess if lo <= s['sw_score'] < hi]
if not subset:
continue
tp = sum(s['pnl'] for s in subset)
w = len([s for s in subset if s['pnl'] > 0])
wr = 100 * w / len(subset)
avg = tp / len(subset)
rt = sum(s['round_trips'] for s in subset)
print(f" {lo}-{hi}{'':<11} {len(subset):>6} {wr:>5.0f}% ${tp:>8.2f} ${avg:>8.4f} {rt:>6}")
# Per-symbol breakdown for best config
print(f"\n 📊 Per-symbol (best adaptive: {best_adaptive}):")
if best_adaptive:
sess = all_results[best_adaptive]['sessions']
syms = sorted(set(s['symbol'] for s in sess))
print(f" {'Symbol':<12} {'Sess':>6} {'WR':>6} {'PnL':>10} {'Avg NATR':>10} {'RTs':>6}")
print(f" {'─'*50}")
for sym in syms:
subset = [s for s in sess if s['symbol'] == sym]
tp = sum(s['pnl'] for s in subset)
w = len([s for s in subset if s['pnl'] > 0])
wr = 100 * w / len(subset)
an = np.mean([s['natr_5m'] for s in subset])
rt = sum(s['round_trips'] for s in subset)
emoji = '🟢' if tp > 0 else '🔴'
print(f" {emoji} {sym:<10} {len(subset):>6} {wr:>5.0f}% ${tp:>8.2f} {an:>9.3f}% {rt:>6}")
# Save
output = {
'config': {
'symbols': SYMBOLS, 'days_back': DAYS_BACK,
'grid_levels': GRID_LEVELS, 'position_size': POSITION_SIZE_USD,
'leverage': LEVERAGE, 'session_candles': SESSION_CANDLES,
'score_threshold': SCORE_THRESHOLD,
},
'spacing_configs': SPACING_CONFIGS,
'summary': summary_data,
'natr_percentiles': {
f'p{p}': round(np.percentile(natr_stats, p), 3)
for p in [10, 25, 50, 75, 90]
} if natr_stats else {},
'tested_at': datetime.now().isoformat(),
}
out_path = Path(__file__).parent / 'results_adaptive_spacing.json'
with open(out_path, 'w') as f:
json.dump(output, f, indent=2)
print(f"\n💾 Saved to {out_path}")
print("Done!")