feat: per-symbol DOTEUR SL/TP override + backtest

config.toml

@@ -7,12 +7,12 @@ base_currency = "EUR"
 auto_select_pair = true
 loop_interval_seconds = 60
 trade_pairs = [ "XXBTZEUR", "XETHZEUR", "SOLEUR", "XXRPZEUR",]
-allocation_per_trade_percent = 95.0
+allocation_per_trade_percent = 20.0
 
 [risk_management]
 max_drawdown_percent = 10.0
 stop_loss_percent = 1.5
-allocation_per_trade_percent = 95.0
+allocation_per_trade_percent = 20.0
 min_buy_score = 0.0
 enable_mean_reversion_signals = true
 enable_trend_breakout_signals = true
@@ -110,6 +110,14 @@ max_consecutive_losses = 2
 loss_streak_pause_minutes = 60
 # NOTE: loss_streak_pause_minutes set to 0 to remove cooling period
 
+[symbols]
+# Per-symbol overrides. Example:
+# [symbols.DOTEUR]
+# intraday_sl_percent = 2.5
+[symbols.DOTEUR]
+intraday_sl_percent = 2.5
+intraday_tp_percent = 3.0
+
 
 [bear_shield]
 enable_bear_shield = false

run_backtest.out

@@ -1,3 +1 @@
-Traceback (most recent call last):
-  File "<stdin>", line 3, in <module>
-FileNotFoundError: [Errno 2] No such file or directory: '$OUT'
+<stdin>:22: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).

tmp_backtest_sweep.py

@@ -0,0 +1,196 @@
+#!/usr/bin/env python3
+import json, os, datetime
+
+BASE = '/mnt/fritz_nas/Volume/kraken/2026'
+SYMBOLS = ['XXRPZEUR','XETHZEUR','ADAEUR','DOTEUR']
+OUT = 'reports/sweep_90d.json'
+
+# helper to read ohlc file (prefer 15m else 5m)
+def load_ohlc(symbol):
+    d = os.path.join(BASE, symbol)
+    if not os.path.isdir(d):
+        return None
+    # prefer ohlc_15m.csv
+    for fname in ['ohlc_15m.csv','ohlc_5m.csv','ohlc_60m.csv']:
+        path = os.path.join(d, fname)
+        if os.path.isfile(path):
+            return path
+    return None
+
+# parse csv into list of candles (ts,open,high,low,close,volume)
+def parse_csv(path):
+    rows = []
+    with open(path,'r') as f:
+        lines = [ln.strip() for ln in f if ln.strip()]
+    if not lines:
+        return []
+    header = [h.strip() for h in lines[0].split(',')]
+    for ln in lines[1:]:
+        parts = ln.split(',')
+        if len(parts) < 5:
+            continue
+        rec = dict(zip(header, parts))
+        try:
+            ts = int(rec.get('ts','0'))
+            o = float(rec.get('open',''))
+            h = float(rec.get('high',''))
+            l = float(rec.get('low',''))
+            c = float(rec.get('close',''))
+        except:
+            continue
+        vol = None
+        try:
+            vol = float(rec.get('volume',''))
+        except:
+            vol = None
+        rows.append({'ts':ts,'dt':datetime.datetime.utcfromtimestamp(ts),'open':o,'high':h,'low':l,'close':c,'volume':vol})
+    return rows
+
+# aggregate 5m->15m if needed
+from collections import defaultdict
+
+def to_15m(rows, src_minutes):
+    if src_minutes == 15:
+        return rows
+    buckets = defaultdict(list)
+    period = src_minutes*60
+    for r in rows:
+        k = (r['ts']//900)*900
+        buckets[k].append(r)
+    agg = []
+    for k in sorted(buckets.keys()):
+        group = buckets[k]
+        opens = [g['open'] for g in group]
+        highs = [g['high'] for g in group]
+        lows = [g['low'] for g in group]
+        closes = [g['close'] for g in group]
+        vols = [g['volume'] for g in group if g['volume'] is not None]
+        agg.append({'ts':k,'dt':datetime.datetime.utcfromtimestamp(k),'open':opens[0],'high':max(highs),'low':min(lows),'close':closes[-1],'volume':sum(vols) if vols else None})
+    return agg
+
+# EMA and backtest logic (same as tmp_backtest_xrp)
+def ema(series_vals, period):
+    k = 2.0/(period+1)
+    out = []
+    s = None
+    for v in series_vals:
+        if s is None:
+            s = v
+        else:
+            s = v*k + s*(1-k)
+        out.append(s)
+    return out
+
+def run_backtest_on_series(series, params):
+    fast_p = 9
+    slow_p = 21
+    closes = [c['close'] for c in series]
+    if len(closes) < slow_p+1:
+        return {'error':'not_enough_bars','bars':len(closes)}
+    ema_fast = ema(closes, fast_p)
+    ema_slow = ema(closes, slow_p)
+    in_pos = False
+    entry_price = None
+    entry_idx = None
+    qty = 0.0
+    cash = 200.0
+    closed = []
+    fee_rate = params['fee_rate']
+    alloc_frac = params['allocation_pct']/100.0
+    sl_pct = params['sl_pct']
+    tp_pct = params['tp_pct']
+    max_hold = 48
+    for i in range(1,len(series)):
+        if not in_pos and ema_fast[i] is not None and ema_slow[i] is not None and ema_fast[i]>ema_slow[i] and ema_fast[i-1]<=ema_slow[i-1]:
+            entry_price = series[i]['open']*(1+0.0008)
+            allocation = cash * alloc_frac
+            if allocation < 1.0:
+                continue
+            qty = (allocation) / entry_price
+            cash -= allocation
+            in_pos = True
+            entry_idx = i
+            continue
+        if in_pos:
+            px_high = series[i]['high']
+            px_low = series[i]['low']
+            tp_price = entry_price*(1+tp_pct/100.0)
+            sl_price = entry_price*(1-sl_pct/100.0)
+            exit_price = None
+            reason = None
+            if px_high>=tp_price and px_low>sl_price:
+                exit_price = min(px_high,tp_price); reason='TP'
+            elif px_low<=sl_price and px_high<tp_price:
+                exit_price = max(px_low,sl_price); reason='SL'
+            elif px_high>=tp_price and px_low<=sl_price:
+                openp = series[i]['open']
+                if abs(tp_price-openp) < abs(openp-sl_price):
+                    exit_price = min(px_high,tp_price); reason='TP_first'
+                else:
+                    exit_price = max(px_low,sl_price); reason='SL_first'
+            elif i-entry_idx >= max_hold:
+                exit_price = series[i]['close']; reason='TIME'
+            if exit_price is not None:
+                exit_price = exit_price*(1-0.0008)
+                gross = (exit_price - entry_price)*qty
+                fee = fee_rate*(entry_price*qty + exit_price*qty)
+                net = gross - fee
+                cash += exit_price*qty - fee
+                closed.append({'entry_idx':entry_idx,'exit_idx':i,'entry_price':entry_price,'exit_price':exit_price,'qty':qty,'pnl':net,'reason':reason})
+                in_pos=False; entry_price=None; entry_idx=None; qty=0.0
+    if in_pos:
+        last = series[-1]['close']
+        exit_price = last*(1-0.0008)
+        gross = (exit_price - entry_price)*qty
+        fee = fee_rate*(entry_price*qty + exit_price*qty)
+        net = gross - fee
+        cash += exit_price*qty - fee
+        closed.append({'entry_idx':entry_idx,'exit_idx':len(series)-1,'entry_price':entry_price,'exit_price':exit_price,'qty':qty,'pnl':net,'reason':'EOD'})
+    net_pnl = cash - 200.0
+    wins = [c for c in closed if c['pnl']>=0]
+    losses = [c for c in closed if c['pnl']<0]
+    eq_hist = [200.0]
+    cur_cash = 200.0
+    for c in closed:
+        cur_cash += c['pnl']
+        eq_hist.append(cur_cash)
+    cur_peak = eq_hist[0]
+    max_dd = 0.0
+    for e in eq_hist:
+        cur_peak = max(cur_peak,e)
+        dd = (cur_peak - e)/cur_peak*100 if cur_peak>0 else 0.0
+        max_dd = max(max_dd,dd)
+    return {'closed_trades':len(closed),'wins':len(wins),'losses':len(losses),'winrate_pct': round(len(wins)/len(closed)*100,2) if closed else 0.0,'net_pnl_eur': round(net_pnl,4),'return_pct': round(net_pnl/200.0*100,2),'max_drawdown_pct': round(max_dd,2)}
+
+# default params
+current_cfg = {'allocation_pct': 20.0, 'sl_pct': 1.5, 'tp_pct': 1.8, 'fee_rate': 0.0026}
+proposed_cfg = {'allocation_pct': 20.0, 'sl_pct': 2.5, 'tp_pct': 3.0, 'fee_rate': 0.0026}
+
+results = {}
+for s in SYMBOLS:
+    path = load_ohlc(s)
+    if not path:
+        results[s] = {'error':'no_data'}
+        continue
+    rows = parse_csv(path)
+    if not rows:
+        results[s] = {'error':'parse_failed'}
+        continue
+    # detect source minutes from filename
+    src_min = 5
+    if '15m' in path:
+        src_min = 15
+    elif '60m' in path:
+        src_min = 60
+    series = to_15m(rows, src_min)
+    # select last ~90 days if available (90*24*4 15m bars)
+    target_bars = 90*24*4
+    use = series[-target_bars:] if len(series) >= target_bars else series
+    res_cur = run_backtest_on_series(use, current_cfg)
+    res_prop = run_backtest_on_series(use, proposed_cfg)
+    results[s] = {'bars_used': len(use), 'current': res_cur, 'proposed': res_prop}
+
+os.makedirs('reports', exist_ok=True)
+with open(OUT,'w') as f:
+    json.dump({'generated': datetime.datetime.utcnow().isoformat() + 'Z', 'symbols': SYMBOLS, 'results': results}, f, indent=2)
+print('wrote', OUT)