""" Baseline: DUAL entry + SL only. NO trailing, NO TP. Compare with Apex Trail to see if trailing adds value. """ import pyarrow.parquet as pq import numpy as np from numba import njit import time as time_mod PT = 0.01; PV = 1.0 print("Loading tickflow_M1...") df = pq.read_table("C:/Users/Black/Downloads/MT5EA/tick_data/tickflow_M1.parquet").to_pandas() tr = np.maximum(df['high']-df['low'], np.maximum(np.abs(df['high']-df['close'].shift(1).fillna(df['close'])), np.abs(df['low']-df['close'].shift(1).fillna(df['close'])))) df['atr'] = (tr.rolling(210, min_periods=14).mean() / PT).fillna(300) df['sp'] = df['spread_avg'] G_c=df['close'].values.astype(np.float64) G_h=df['high'].values.astype(np.float64) G_l=df['low'].values.astype(np.float64) G_ac=df['ask_close'].values.astype(np.float64) G_ao=df['ask_open'].values.astype(np.float64) G_o=df['open'].values.astype(np.float64) G_sp=df['sp'].values.astype(np.float64) G_atr=df['atr'].values.astype(np.float64) G_tc=df['tick_count'].values.astype(np.float64) N=len(df) del df @njit(cache=True) def sim_no_trail(c,h,l,ac,ao,o,sp,atr,tc,N, sl_atr, entry_interval, tick_thresh): MX=60000 p_s=np.zeros(MX,np.int8); p_e=np.zeros(MX,np.float64) p_atr=np.zeros(MX,np.float64); p_op=np.zeros(MX,np.bool_) p_pnl=np.zeros(MX,np.float64) pc=0; last_e=-999 for i in range(N): B=c[i]; A=ac[i]; BH=h[i]; BL=l[i]; SP=sp[i]; ATR=atr[i] AH=BH+SP*PT for j in range(pc): if not p_op[j]: continue si=p_s[j]; en=p_e[j]; sl_pts=p_atr[j]*sl_atr if si==1: pp_w=(BL-en)/PT else: pp_w=(en-AH)/PT if pp_w<=-sl_pts: p_op[j]=False; p_pnl[j]=-(sl_pts*PV/100.0) if i-last_e>=entry_interval and SP<40 and pc+2<=MX: if tc[i]>=tick_thresh: last_e=i for k in range(2): si2=1 if k==0 else -1 ep=ao[i] if si2==1 else o[i] p_s[pc]=si2; p_e[pc]=ep; p_atr[pc]=ATR p_op[pc]=True; p_pnl[pc]=0.0; pc+=1 else: last_e=i for j in range(pc): if p_op[j]: si=p_s[j] if si==1: ppts=(c[N-1]-p_e[j])/PT else: ppts=(p_e[j]-ac[N-1])/PT p_pnl[j]=ppts*PV/100.0; p_op[j]=False return p_pnl[:pc] # Same with Apex Trail best params @njit(cache=True) def sim_apex(c,h,l,ac,ao,o,sp,tv,atr,adx,tc,N, sl_atr,trail_start,p2_trend,p2_norm,p2_side,p3_sq, entry_interval,tick_thresh,tv_exhaust,tv_recover): MX=60000 p_s=np.zeros(MX,np.int8); p_e=np.zeros(MX,np.float64) p_sl=np.zeros(MX,np.float64); p_ph=np.zeros(MX,np.int8) p_pk=np.zeros(MX,np.float64); p_pkp=np.zeros(MX,np.float64) p_op=np.zeros(MX,np.bool_); p_pnl=np.zeros(MX,np.float64) p_atr_e=np.zeros(MX,np.float64) pc=0; last_e=-999 for i in range(N): B=c[i]; A=ac[i]; BH=h[i]; BL=l[i] SP=sp[i]; TV=tv[i]; ATR=atr[i]; ADX=adx[i] AH=BH+SP*PT; AL=BL+SP*PT for j in range(pc): if not p_op[j]: continue si=p_s[j]; en=p_e[j]; ea=p_atr_e[j] sl_pts=ea*sl_atr if si==1: pp_w=(BL-en)/PT; pp_b=(BH-en)/PT else: pp_w=(en-AH)/PT; pp_b=(en-AL)/PT if pp_b>p_pkp[j]: p_pkp[j]=pp_b p_pk[j]=BH if si==1 else AL if pp_w<=-sl_pts: p_op[j]=False; p_pnl[j]=-(sl_pts*PV/100.0); continue if p_ph[j]==0 and pp_b>=ATR*trail_start: p_ph[j]=2 m=p2_trend if ADX>30 else (p2_norm if ADX>20 else p2_side) pk=p_pk[j] if si==1: ns=pk-ATR*m*PT; ns=max(ns,en); p_sl[j]=ns else: ns=pk+ATR*m*PT; ns=min(ns,en); p_sl[j]=ns if p_ph[j]==2: m=p2_trend if ADX>30 else (p2_norm if ADX>20 else p2_side) td=ATR*m*PT; pk=p_pk[j] if si==1: ns=pk-td; ns=max(ns,en) if ns>p_sl[j]: p_sl[j]=ns else: ns=pk+td; ns=min(ns,en) if p_sl[j]<=0 or nsATR*1.5: p_ph[j]=3 if p_ph[j]==3: sq=ATR*p3_sq*PT; pk=p_pk[j] if si==1: ns=pk-sq; ns=max(ns,en) if ns>p_sl[j]: p_sl[j]=ns else: ns=pk+sq; ns=min(ns,en) if p_sl[j]<=0 or nstv_recover: p_ph[j]=2 if p_ph[j]>=2 and p_sl[j]>0: hit=False if si==1 and BL<=p_sl[j]: hit=True elif si==-1 and AH>=p_sl[j]: hit=True if hit: ppts=(p_sl[j]-en)/PT if si==1 else (en-p_sl[j])/PT p_op[j]=False; p_pnl[j]=ppts*PV/100.0 if i-last_e>=entry_interval and SP<40 and pc+2<=MX: if tc[i]>=tick_thresh: last_e=i for k in range(2): si2=1 if k==0 else -1 ep=ao[i] if si2==1 else o[i] p_s[pc]=si2; p_e[pc]=ep sl_d=ATR*sl_atr*PT p_sl[pc]=ep-sl_d if si2==1 else ep+sl_d p_ph[pc]=0; p_pk[pc]=ep; p_pkp[pc]=0.0 p_op[pc]=True; p_pnl[pc]=0.0; p_atr_e[pc]=ATR; pc+=1 else: last_e=i for j in range(pc): if p_op[j]: si=p_s[j] ppts=(c[N-1]-p_e[j])/PT if si==1 else (p_e[j]-ac[N-1])/PT p_pnl[j]=ppts*PV/100.0; p_op[j]=False return p_pnl[:pc] # Need TV and ADX for apex df2 = pq.read_table("C:/Users/Black/Downloads/MT5EA/tick_data/tickflow_M1.parquet").to_pandas() tr2 = np.maximum(df2['high']-df2['low'], np.maximum(np.abs(df2['high']-df2['close'].shift(1).fillna(df2['close'])), np.abs(df2['low']-df2['close'].shift(1).fillna(df2['close'])))) df2['atr'] = (tr2.rolling(210, min_periods=14).mean() / PT).fillna(300) dm = df2['close'].diff().abs() / PT df2['adx'] = (dm.rolling(210, min_periods=14).mean() / df2['atr'].clip(lower=1) * 50).clip(upper=60).fillna(25) df2['tv'] = df2['tick_count'].rolling(5, min_periods=1).mean() / 60.0 G_tv=df2['tv'].values.astype(np.float64) G_adx=df2['adx'].values.astype(np.float64) del df2 tick25 = float(np.percentile(G_tc, 25)) print("JIT warmup...") _ = sim_no_trail(G_c,G_h,G_l,G_ac,G_ao,G_o,G_sp,G_atr,G_tc,N, 2.0, 3, tick25) _ = sim_apex(G_c,G_h,G_l,G_ac,G_ao,G_o,G_sp,G_tv,G_atr,G_adx,G_tc,N, 2.0,3.0,2.25,0.75,0.7,0.2, 3,tick25,0.75,8.0) def report(name, pnls): n=len(pnls); wins=int(np.sum(pnls>0)); losses=n-wins ws=float(np.sum(pnls[pnls>0])); ls=float(np.sum(pnls[pnls<=0])) aw=float(np.mean(pnls[pnls>0])) if wins else 0 al=float(np.mean(pnls[pnls<=0])) if losses else 0 pf=abs(ws)/max(0.01,abs(ls)) exp=float(np.mean(pnls)) wr=wins/n*100 print(f"\n {name}") print(f" Trades:{n} WR:{wr:.1f}% PF:{pf:.2f} Exp:${exp:.2f}") print(f" AvgW:${aw:.2f} AvgL:${al:.2f} W/L:{abs(aw/al) if al else 0:.2f}") print(f" Total:${np.sum(pnls):.0f} | Balance:${500+np.sum(pnls):.0f}") return pf, exp, np.sum(pnls) print(f"\n{'='*60}") print(f"COMPARISON: No Trail vs Apex Trail (Optuna Best)") print(f"{'='*60}") # 1. NO TRAIL - SL only t0=time_mod.time() pnls1 = sim_no_trail(G_c,G_h,G_l,G_ac,G_ao,G_o,G_sp,G_atr,G_tc,N, 2.0, 3, tick25) t1=time_mod.time()-t0 pf1, exp1, tot1 = report("A) NO TRAILING (SL=2xATR only)", pnls1) # 2. APEX TRAIL (Optuna best: SL=2, TS=3, P2t=2.25, P2n=0.75, P2s=0.7, P3=0.2, BE=True) t0=time_mod.time() pnls2 = sim_apex(G_c,G_h,G_l,G_ac,G_ao,G_o,G_sp,G_tv,G_atr,G_adx,G_tc,N, 2.0, 3.0, 2.25, 0.75, 0.7, 0.2, 3, tick25, 0.75, 8.0) t2=time_mod.time()-t0 pf2, exp2, tot2 = report("B) APEX TRAIL (Optuna Best)", pnls2) # 3. Simple TP = 1xATR, SL = 2xATR (classic 1:2 risk-reward reversed) @njit(cache=True) def sim_fixed_tp(c,h,l,ac,ao,o,sp,atr,tc,N, sl_atr, tp_atr, entry_interval, tick_thresh): MX=60000 p_s=np.zeros(MX,np.int8); p_e=np.zeros(MX,np.float64) p_atr=np.zeros(MX,np.float64); p_op=np.zeros(MX,np.bool_) p_pnl=np.zeros(MX,np.float64) pc=0; last_e=-999 for i in range(N): B=c[i]; A=ac[i]; BH=h[i]; BL=l[i]; SP=sp[i]; ATR=atr[i] AH=BH+SP*PT; AL=BL+SP*PT for j in range(pc): if not p_op[j]: continue si=p_s[j]; en=p_e[j]; ea=p_atr[j] sl_pts=ea*sl_atr; tp_pts=ea*tp_atr if si==1: pp_w=(BL-en)/PT; pp_b=(BH-en)/PT else: pp_w=(en-AH)/PT; pp_b=(en-AL)/PT if pp_w<=-sl_pts: p_op[j]=False; p_pnl[j]=-(sl_pts*PV/100.0); continue if pp_b>=tp_pts: p_op[j]=False; p_pnl[j]=tp_pts*PV/100.0 if i-last_e>=entry_interval and SP<40 and pc+2<=MX: if tc[i]>=tick_thresh: last_e=i for k in range(2): si2=1 if k==0 else -1 ep=ao[i] if si2==1 else o[i] p_s[pc]=si2; p_e[pc]=ep; p_atr[pc]=ATR p_op[pc]=True; p_pnl[pc]=0.0; pc+=1 else: last_e=i for j in range(pc): if p_op[j]: si=p_s[j] ppts=(c[N-1]-p_e[j])/PT if si==1 else (p_e[j]-ac[N-1])/PT p_pnl[j]=ppts*PV/100.0; p_op[j]=False return p_pnl[:pc] # Warmup _ = sim_fixed_tp(G_c,G_h,G_l,G_ac,G_ao,G_o,G_sp,G_atr,G_tc,N, 2.0, 0.5, 3, tick25) # Test multiple TP/SL ratios print(f"\n{'='*60}") print(f"C) FIXED TP/SL GRID SEARCH") print(f"{'='*60}") best_pf = 0; best_combo = "" for sl in [1.0, 1.5, 2.0, 3.0]: for tp in [0.3, 0.5, 1.0, 1.5, 2.0, 3.0]: pnls3 = sim_fixed_tp(G_c,G_h,G_l,G_ac,G_ao,G_o,G_sp,G_atr,G_tc,N, sl, tp, 3, tick25) n=len(pnls3); ws=float(np.sum(pnls3[pnls3>0])); ls=float(np.sum(pnls3[pnls3<=0])) pf=abs(ws)/max(0.01,abs(ls)); wins=int(np.sum(pnls3>0)); wr=wins/n*100 exp=float(np.mean(pnls3)); tot=float(np.sum(pnls3)) flag = " <<<" if pf > best_pf else "" if pf > best_pf: best_pf=pf; best_combo=f"SL={sl} TP={tp}" print(f" SL={sl:.1f}xATR TP={tp:.1f}xATR | WR:{wr:.0f}% PF:{pf:.2f} Exp:${exp:.2f} Total:${tot:.0f}{flag}") print(f"\n BEST: {best_combo} | PF={best_pf:.2f}") print(f"\n{'='*60}") print(f"SUMMARY") print(f"{'='*60}") print(f" A) No Trail: PF={pf1:.2f} PnL=${tot1:.0f}") print(f" B) Apex Trail: PF={pf2:.2f} PnL=${tot2:.0f}") print(f" C) Best TP/SL: PF={best_pf:.2f} ({best_combo})")