← Назад"""
Range Grid Backtest — растянутая сетка на весь боковик
Стратегия Rick'а (классика):
1. Находим боковик на 1H (range 5-20%)
2. Растягиваем 20-40 гридов на весь диапазон
3. Крутим round-trips пока цена внутри
4. Когда пробой — фиксим всё, ищем другую монету
5. Мультимонетный — до 3 монет одновременно
Тестируем варианты:
A) 20 levels, range detection 48H lookback
B) 30 levels, range detection 48H
C) 40 levels, range detection 48H
D) 30 levels, range detection 72H (шире диапазон)
+ Sideways filter score >= 45 для входа
+ Exit: price breaks range ±1% buffer
Usage: python3 backtest_range_grid.py
"""
import requests
import pandas as pd
import numpy as np
import time
import json
from datetime import datetime
from pathlib import Path
# ============================================================
# CONFIG
# ============================================================
SYMBOLS = [
"ETHUSDT", "DOGEUSDT", "PENGUUSDT", "ENAUSDT",
"NEARUSDT", "WLDUSDT", "SOLUSDT", "ARBUSDT",
"XRPUSDT", "LINKUSDT", "SUIUSDT", "OPUSDT",
"ADAUSDT", "UNIUSDT", "AVAXUSDT",
]
DAYS_BACK = 30 # longer period — need time for ranges to form and play out
LEVERAGE = 10
DEPOSIT = 50.0
FEE_PCT = 0.02 / 100 # maker
MAX_LOSS_PCT = 5.0 # wider stop for range grid
# Screener
BB_PERIOD = 20
BB_STD = 2.0
ADX_PERIOD = 14
RANGE_LOOKBACK_1H = 24 # for sideways score
# Range grid variants
CONFIGS = [
{'name': 'A: 20lvl 48h', 'levels': 20, 'range_hours': 48, 'pos_usd': 2.5},
{'name': 'B: 30lvl 48h', 'levels': 30, 'range_hours': 48, 'pos_usd': 1.5},
{'name': 'C: 40lvl 48h', 'levels': 40, 'range_hours': 48, 'pos_usd': 1.0},
{'name': 'D: 30lvl 72h', 'levels': 30, 'range_hours': 72, 'pos_usd': 1.5},
{'name': 'E: 20lvl 72h', 'levels': 20, 'range_hours': 72, 'pos_usd': 2.5},
]
BREAKOUT_BUFFER_PCT = 1.0 # exit when price breaks range ± 1%
MIN_RANGE_PCT = 4.0 # min range to consider (too tight = no profit)
MAX_RANGE_PCT = 25.0 # max range (too wide = probably trending)
MAX_SESSION_HOURS = 168 # max 7 days per session
COOLDOWN_HOURS = 2 # gap between sessions per symbol
# ============================================================
# DATA
# ============================================================
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:
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
# ============================================================
# SIDEWAYS SCREENER (1H)
# ============================================================
def calc_screener_1h(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
h, l, c = df['high'], df['low'], df['close']
plus_dm = h.diff()
minus_dm = -l.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)
tr = pd.concat([h - l, (h - c.shift(1)).abs(), (l - c.shift(1)).abs()], 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 / c) * 100
df['range_high'] = df['high'].rolling(RANGE_LOOKBACK_1H).max()
df['range_low'] = df['low'].rolling(RANGE_LOOKBACK_1H).min()
rng = df['range_high'] - df['range_low']
df['range_pos'] = (df['close'] - df['range_low']) / rng.replace(0, np.nan)
df['range_pct'] = (rng / df['close']) * 100
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_1H).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
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
if 1.5 <= bb_w <= 4.0:
score += 20 * (bb_w - 1.5) / 1.0 if bb_w <= 2.5 else 20 * (4.0 - bb_w) / 1.5
elif 0.5 <= bb_w < 1.5:
score += 5
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
max_ch = RANGE_LOOKBACK_1H * 0.7
if dc >= 8:
score += min(20, 20 * (dc - 8) / (max_ch - 8))
if 0.15 <= natr <= 0.6:
score += 15 * (natr - 0.15) / 0.15 if natr <= 0.3 else 15 * (0.6 - natr) / 0.3
elif 0.1 <= natr < 0.15:
score += 3
return max(0, round(score, 1))
# ============================================================
# RANGE GRID ENGINE — runs on 1H candles
# ============================================================
def run_range_grid(df_1h, start_idx, n_levels, range_hours, pos_usd):
"""
1. Look back range_hours to find high/low
2. Set grid levels across that range
3. Simulate on 1H candles until breakout or timeout
"""
lookback = range_hours # 1H candles = hours
if start_idx < lookback + 5 or start_idx + 5 >= len(df_1h):
return None
# Detect range from lookback
window = df_1h.iloc[start_idx - lookback:start_idx]
range_high = window['high'].max()
range_low = window['low'].min()
range_pct = ((range_high - range_low) / range_low) * 100
if range_pct < MIN_RANGE_PCT or range_pct > MAX_RANGE_PCT:
return None
current_price = df_1h['close'].iloc[start_idx]
# Check price is within range (not already broken out)
if current_price > range_high or current_price < range_low:
return None
# Grid levels spread across entire range
step = (range_high - range_low) / (n_levels + 1)
grid_levels = [range_low + step * (k + 1) for k in range(n_levels)]
# Split into buy levels (below price) and sell levels (above price)
buy_levels = sorted([lv for lv in grid_levels if lv < current_price])
sell_levels = sorted([lv for lv in grid_levels if lv >= current_price])
# Track state
buy_filled = {lv: False for lv in buy_levels}
sell_filled = {lv: False for lv in sell_levels}
positions = [] # (side, entry_price)
pnl = 0.0
fees = 0.0
trades = 0
round_trips = 0
close_reason = 'timeout'
# Breakout thresholds
break_high = range_high * (1 + BREAKOUT_BUFFER_PCT / 100)
break_low = range_low * (1 - BREAKOUT_BUFFER_PCT / 100)
max_candles = min(MAX_SESSION_HOURS, len(df_1h) - start_idx - 1)
for j in range(start_idx + 1, start_idx + max_candles):
price = df_1h['close'].iloc[j]
lo = df_1h['low'].iloc[j]
hi = df_1h['high'].iloc[j]
# Check buy fills (price dipped to level)
for lv in buy_levels:
if not buy_filled[lv] and lo <= lv:
buy_filled[lv] = True
positions.append(('long', lv))
fees += pos_usd * LEVERAGE * FEE_PCT
trades += 1
# Check sell fills (price rose to level)
for lv in sell_levels:
if not sell_filled[lv] and hi >= lv:
sell_filled[lv] = True
positions.append(('short', lv))
fees += pos_usd * LEVERAGE * FEE_PCT
trades += 1
# Round-trip matching:
# Pair closest buy with closest sell that's above it
# Sort positions
longs = sorted([p for p in positions if p[0] == 'long'], key=lambda x: x[1])
shorts = sorted([p for p in positions if p[0] == 'short'], key=lambda x: x[1])
matched_longs = set()
matched_shorts = set()
for li, (_, buy_px) in enumerate(longs):
for si, (_, sell_px) in enumerate(shorts):
if si in matched_shorts:
continue
if sell_px > buy_px:
# Round trip!
qty = (pos_usd * LEVERAGE) / buy_px
spread = sell_px - buy_px
pnl += qty * spread
fees += pos_usd * LEVERAGE * FEE_PCT * 2 # close fees
round_trips += 1
matched_longs.add(li)
matched_shorts.add(si)
# Reset these levels so they can fill again
buy_filled[buy_px] = False
sell_filled[sell_px] = False
break
# Remove matched positions
new_positions = []
for idx, p in enumerate(longs):
if idx not in matched_longs:
new_positions.append(p)
for idx, p in enumerate(shorts):
if idx not in matched_shorts:
new_positions.append(p)
positions = new_positions
# BREAKOUT CHECK — exit all
if price > break_high or price < break_low:
# Close all open positions at current price
for side, entry_px in positions:
qty = (pos_usd * LEVERAGE) / entry_px
if side == 'long':
pnl += qty * (price - entry_px)
else:
pnl += qty * (entry_px - price)
fees += pos_usd * LEVERAGE * FEE_PCT
positions = []
if price > break_high:
close_reason = 'breakout_up'
else:
close_reason = 'breakout_down'
break
# Max loss check
unrealized = 0
for side, entry_px in positions:
qty = (pos_usd * LEVERAGE) / entry_px
if side == 'long':
unrealized += qty * (price - entry_px)
else:
unrealized += qty * (entry_px - price)
total_capital = n_levels * pos_usd
if pnl + unrealized - fees < -(total_capital * MAX_LOSS_PCT / 100):
for side, entry_px in positions:
qty = (pos_usd * LEVERAGE) / entry_px
if side == 'long':
pnl += qty * (price - entry_px)
else:
pnl += qty * (entry_px - price)
fees += pos_usd * LEVERAGE * FEE_PCT
positions = []
close_reason = 'max_loss'
break
# Close remaining at end
if positions:
price = df_1h['close'].iloc[min(start_idx + max_candles - 1, len(df_1h) - 1)]
for side, entry_px in positions:
qty = (pos_usd * LEVERAGE) / entry_px
if side == 'long':
pnl += qty * (price - entry_px)
else:
pnl += qty * (entry_px - price)
fees += pos_usd * LEVERAGE * FEE_PCT
net = pnl - fees
duration_h = j - start_idx if 'j' in dir() else 0
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,
'range_pct': round(range_pct, 2),
'duration_h': duration_h,
'levels_used': n_levels,
'spacing_pct': round(range_pct / (n_levels + 1), 3),
}
# ============================================================
# MULTI-COIN ROTATION SIMULATOR
# ============================================================
def simulate_rotation(all_sessions_by_symbol, max_concurrent=3):
"""
Simulate multi-coin rotation:
- Sort all potential sessions by start time
- Allow max N concurrent grids
- When one exits, start next available
"""
# Flatten and sort by start time
all_entries = []
for sym, sessions in all_sessions_by_symbol.items():
for s in sessions:
all_entries.append({**s, 'symbol': sym})
all_entries.sort(key=lambda x: x['start_idx'])
active = [] # (end_idx, session_data)
completed = []
for entry in all_entries:
# Remove finished sessions
active = [(end, s) for end, s in active if end > entry['start_idx']]
if len(active) < max_concurrent:
end_idx = entry['start_idx'] + entry['duration_h']
active.append((end_idx, entry))
completed.append(entry)
return completed
# ============================================================
# MAIN
# ============================================================
if __name__ == "__main__":
print("=" * 75)
print(" RANGE GRID BACKTEST — full range × 20-40 levels")
print(f" {len(SYMBOLS)} coins | {DAYS_BACK}d | ${DEPOSIT} × {LEVERAGE}x")
print("=" * 75)
# Store results
config_results = {c['name']: {'all': [], 'filtered': [], 'by_symbol': {}} for c in CONFIGS}
for sym in SYMBOLS:
print(f"\n 📊 {sym}...", end=" ", flush=True)
df_1h = fetch_klines(sym, '1h', DAYS_BACK)
if len(df_1h) < 100:
print("skip")
continue
# Calc screener
df_1h = calc_screener_1h(df_1h)
df_1h['sw_score'] = df_1h.apply(sideways_score, axis=1)
for cfg in CONFIGS:
n_levels = cfg['levels']
range_hours = cfg['range_hours']
pos_usd = cfg['pos_usd']
cname = cfg['name']
# Scan every 4 hours for entry opportunities
i = max(range_hours + 10, 50)
sessions = []
cooldown_until = 0
while i < len(df_1h) - 5:
if i < cooldown_until:
i += 1
continue
score = df_1h['sw_score'].iloc[i]
adx = df_1h['adx'].iloc[i]
range_pos = df_1h['range_pos'].iloc[i] if not pd.isna(df_1h['range_pos'].iloc[i]) else 0.5
result = run_range_grid(df_1h, i, n_levels, range_hours, pos_usd)
if result:
result['symbol'] = sym
result['sw_score'] = round(score, 1)
result['adx'] = round(adx, 1) if not pd.isna(adx) else None
result['range_pos'] = round(range_pos, 2)
result['start_idx'] = i
result['ts'] = str(df_1h['timestamp'].iloc[i])
config_results[cname]['all'].append(result)
if sym not in config_results[cname]['by_symbol']:
config_results[cname]['by_symbol'][sym] = []
config_results[cname]['by_symbol'][sym].append(result)
if score >= 40:
config_results[cname]['filtered'].append(result)
# Skip ahead by session duration (don't overlap)
cooldown_until = i + max(result['duration_h'], COOLDOWN_HOURS)
i += 4 # check every 4 hours
print(f"done")
# ============================================================
# REPORTS
# ============================================================
print("\n" + "=" * 75)
print(" 🏆 RESULTS")
print("=" * 75)
print(f"\n {'Config':<20} {'Mode':<6} {'Sess':>5} {'WR':>6} {'PnL':>10} {'Avg':>9} {'$/wk':>7} {'RTs':>5} {'AvgRng':>7} {'AvgDur':>7} {'ML':>4}")
print(f" {'─'*90}")
best_name = None
best_avg = -999
for cfg in CONFIGS:
cname = cfg['name']
for mode, label in [('all', 'ALL'), ('filtered', 'FILT')]:
sess = config_results[cname][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 = total_pnl / len(sess)
rts = sum(s['round_trips'] for s in sess)
ml = len([s for s in sess if s['close_reason'] == 'max_loss'])
avg_range = np.mean([s['range_pct'] for s in sess])
avg_dur = np.mean([s['duration_h'] for s in sess])
per_week = total_pnl / DAYS_BACK * 7
emoji = '🟢' if total_pnl > 0 else '🔴'
print(f" {emoji} {cname:<18} {label:<6} {len(sess):>5} {wr:>5.0f}% ${total_pnl:>8.2f} ${avg:>7.3f} ${per_week:>5.2f} {rts:>5} {avg_range:>6.1f}% {avg_dur:>5.0f}h {ml:>4}")
if label == 'FILT' and avg > best_avg:
best_avg = avg
best_name = cname
# Best config deep-dive
if best_name:
sess = config_results[best_name]['filtered']
print(f"\n{'='*75}")
print(f" 🥇 ЛУЧШИЙ (filtered): {best_name}")
print(f"{'='*75}")
total_pnl = sum(s['pnl'] for s in sess)
wins = [s for s in sess if s['pnl'] > 0]
losses = [s for s in sess if s['pnl'] <= 0]
print(f" Sessions: {len(sess)} | WR: {100*len(wins)/len(sess):.0f}%")
print(f" Total PnL: ${total_pnl:.2f} | Per week: ${total_pnl/DAYS_BACK*7:.2f}")
print(f" Per month (est): ${total_pnl/DAYS_BACK*30:.2f}")
if wins:
print(f" Avg win: ${sum(s['pnl'] for s in wins)/len(wins):.3f} | Best: ${max(s['pnl'] for s in wins):.3f}")
if losses:
print(f" Avg loss: ${sum(s['pnl'] for s in losses)/len(losses):.3f} | Worst: ${min(s['pnl'] for s in losses):.3f}")
gross_w = sum(s['pnl'] for s in wins) if wins else 0
gross_l = abs(sum(s['pnl'] for s in losses)) if losses else 1
print(f" Profit factor: {gross_w/gross_l:.2f}")
print(f" Avg range: {np.mean([s['range_pct'] for s in sess]):.1f}%")
print(f" Avg duration: {np.mean([s['duration_h'] for s in sess]):.0f}h")
print(f" Avg spacing: {np.mean([s['spacing_pct'] for s in sess]):.3f}%")
print(f" Total round-trips: {sum(s['round_trips'] for s in sess)}")
# Close reasons
reasons = {}
for s in sess:
r = s['close_reason']
reasons[r] = reasons.get(r, 0) + 1
print(f" Close reasons: {reasons}")
# Equity curve
equity = [0]
for s in sess:
equity.append(equity[-1] + s['pnl'])
eq = np.array(equity)
peak = np.maximum.accumulate(eq)
dd = eq - peak
print(f" Max drawdown: ${dd.min():.3f}")
# By range size
print(f"\n PnL by range size:")
print(f" {'Range':<12} {'Sess':>5} {'WR':>6} {'PnL':>10} {'Avg':>9} {'RTs':>5} {'AvgDur':>7}")
print(f" {'─'*56}")
for lo, hi in [(4, 7), (7, 10), (10, 15), (15, 20), (20, 25)]:
sub = [s for s in sess if lo <= s['range_pct'] < hi]
if not sub:
continue
tp = sum(s['pnl'] for s in sub)
w = len([s for s in sub if s['pnl'] > 0])
wr = 100 * w / len(sub)
rt = sum(s['round_trips'] for s in sub)
ad = np.mean([s['duration_h'] for s in sub])
e = '🟢' if tp > 0 else '🔴'
print(f" {e} {lo}-{hi}%{'':<7} {len(sub):>5} {wr:>5.0f}% ${tp:>8.2f} ${tp/len(sub):>7.3f} {rt:>5} {ad:>5.0f}h")
# Per symbol
print(f"\n Per symbol:")
print(f" {'Symbol':<12} {'Sess':>5} {'WR':>6} {'PnL':>10} {'RTs':>5} {'AvgRng':>7}")
print(f" {'─'*45}")
syms = sorted(set(s['symbol'] for s in sess))
for sym in syms:
sub = [s for s in sess if s['symbol'] == sym]
tp = sum(s['pnl'] for s in sub)
w = len([s for s in sub if s['pnl'] > 0])
wr = 100 * w / len(sub)
rt = sum(s['round_trips'] for s in sub)
ar = np.mean([s['range_pct'] for s in sub])
e = '🟢' if tp > 0 else '🔴'
print(f" {e} {sym:<10} {len(sub):>5} {wr:>5.0f}% ${tp:>8.2f} {rt:>5} {ar:>6.1f}%")
# By close reason detail
print(f"\n PnL by close reason:")
for reason in sorted(reasons.keys()):
sub = [s for s in sess if s['close_reason'] == reason]
tp = sum(s['pnl'] for s in sub)
avg = tp / len(sub) if sub else 0
print(f" {reason:<20} {len(sub):>4} sess | ${tp:>8.3f} total | ${avg:>7.3f} avg")
# Multi-coin rotation simulation (best config)
if best_name:
print(f"\n{'='*75}")
print(f" 🔄 MULTI-COIN ROTATION (max 3 concurrent)")
print(f"{'='*75}")
by_sym = config_results[best_name]['by_symbol']
# Filter each symbol's sessions
filtered_by_sym = {}
for sym, sessions in by_sym.items():
filtered_by_sym[sym] = [s for s in sessions if s.get('sw_score', 0) >= 40]
rotated = simulate_rotation(filtered_by_sym, max_concurrent=3)
if rotated:
tp = sum(s['pnl'] for s in rotated)
w = len([s for s in rotated if s['pnl'] > 0])
wr = 100 * w / len(rotated)
rt = sum(s['round_trips'] for s in rotated)
print(f" Sessions taken: {len(rotated)}")
print(f" WR: {wr:.0f}% | Total PnL: ${tp:.2f} | Per week: ${tp/DAYS_BACK*7:.2f}")
print(f" Round-trips: {rt}")
coins_used = set(s['symbol'] for s in rotated)
print(f" Coins rotated: {len(coins_used)} ({', '.join(sorted(coins_used))})")
# Top 10 sessions
if best_name:
sess = config_results[best_name]['filtered']
print(f"\n 📈 Top 10 sessions:")
sorted_s = sorted(sess, key=lambda x: x['pnl'], reverse=True)[:10]
for s in sorted_s:
e = '🟢' if s['pnl'] > 0 else '🔴'
print(f" {e} {s['symbol']:<10} {s['ts'][:13]} | rng {s['range_pct']}% | {s['duration_h']}h | {s['round_trips']}rt | ${s['pnl']:.3f} | {s['close_reason']}")
print(f"\n 📉 Worst 5:")
sorted_s = sorted(sess, key=lambda x: x['pnl'])[:5]
for s in sorted_s:
print(f" 🔴 {s['symbol']:<10} {s['ts'][:13]} | rng {s['range_pct']}% | {s['duration_h']}h | {s['round_trips']}rt | ${s['pnl']:.3f} | {s['close_reason']}")
# Save
output = {
'config': {
'symbols': SYMBOLS, 'days_back': DAYS_BACK, 'leverage': LEVERAGE,
'fee_pct': FEE_PCT * 100, 'breakout_buffer': BREAKOUT_BUFFER_PCT,
'min_range': MIN_RANGE_PCT, 'max_range': MAX_RANGE_PCT,
},
'configs_tested': CONFIGS,
'results': {name: {
'all_count': len(config_results[name]['all']),
'all_pnl': round(sum(s['pnl'] for s in config_results[name]['all']), 4) if config_results[name]['all'] else 0,
'filtered_count': len(config_results[name]['filtered']),
'filtered_pnl': round(sum(s['pnl'] for s in config_results[name]['filtered']), 4) if config_results[name]['filtered'] else 0,
} for name in config_results},
'best': best_name,
'tested_at': datetime.now().isoformat(),
}
out_path = Path(__file__).parent / 'results_range_grid.json'
with open(out_path, 'w') as f:
json.dump(output, f, indent=2)
print(f"\n💾 {out_path}")