185 lines
7.7 KiB
Python
185 lines
7.7 KiB
Python
"""
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EMA趋势策略 - 精准优化（~50组参数，约2分钟）
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已知基线：EMA(8/21/120) ATR>0.03% SL=0.4% MH=1800s
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→ 14066 trades, dirPnL +327, netFee 428, net -101
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核心优化思路：
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1. 提高 ATR 门槛 → 减少交易次数，只在波动大时交易
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2. 加长 EMA 周期 → 减少交叉频率
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3. 放宽止损 → 让趋势有更多空间发展
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4. 延长最大持仓 → 捕获更大行情
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"""
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import sys, time, datetime, sqlite3
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from pathlib import Path
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class EMA:
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__slots__ = ('k', 'v')
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def __init__(self, p):
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self.k = 2.0 / (p + 1); self.v = None
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def update(self, x):
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self.v = x if self.v is None else x * self.k + self.v * (1 - self.k)
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return self.v
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def load():
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db = Path(__file__).parent.parent / 'models' / 'database.db'
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s = int(datetime.datetime(2025,1,1).timestamp()) * 1000
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e = int(datetime.datetime(2026,1,1).timestamp()) * 1000
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conn = sqlite3.connect(str(db))
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rows = conn.cursor().execute(
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"SELECT id,open,high,low,close FROM bitmart_eth_1m WHERE id>=? AND id<? ORDER BY id", (s,e)
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).fetchall()
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conn.close()
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return [(datetime.datetime.fromtimestamp(r[0]/1000.0), r[1], r[2], r[3], r[4]) for r in rows]
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def bt(data, fp, sp, bp, atr_min, sl_pct, mh):
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bal=1000.0; pos=0; op=0.0; ot=None; ps=0.0; pend=None
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ef=EMA(fp); es=EMA(sp); eb=EMA(bp)
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H=[]; L=[]; C=[]
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pf_=None; ps_=None
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tc=0; wc=0; dpnl=0.0; tfee=0.0; treb=0.0
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hsl=sl_pct*1.5; AP=14
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def _close(price, dt_):
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nonlocal bal,pos,op,ot,ps,pend,tc,wc,dpnl,tfee,treb
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pp = (price-op)/op if pos==1 else (op-price)/op
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pnl_=ps*pp; cv=ps*(1+pp); cf=cv*0.0006; of_=ps*0.0006; tt=of_+cf; rb=tt*0.9
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bal+=pnl_-cf+rb; dpnl+=pnl_; tfee+=tt; treb+=rb
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tc+=1; wc+=(1 if pnl_>0 else 0)
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pos=0; op=0; ot=None; ps=0; pend=None
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def _open(d, price, dt_):
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nonlocal bal,pos,op,ot,ps
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ns=bal*0.005*50
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if ns<1: return
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bal-=ns*0.0006
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pos=1 if d=='L' else -1; op=price; ot=dt_; ps=ns
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for dt,o_,h_,l_,c_ in data:
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p=c_; H.append(h_); L.append(l_); C.append(p)
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fast=ef.update(p); slow=es.update(p); big=eb.update(p)
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atr_pct=0.0
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if len(H)>AP+1:
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s=0.0
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for i in range(-AP,0):
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tr=H[i]-L[i]; d1=abs(H[i]-C[i-1]); d2=abs(L[i]-C[i-1])
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if d1>tr: tr=d1
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if d2>tr: tr=d2
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s+=tr
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atr_pct=s/(AP*p) if p>0 else 0
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cu=pf_ is not None and pf_<=ps_ and fast>slow
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cd=pf_ is not None and pf_>=ps_ and fast<slow
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pf_=fast; ps_=slow
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if pos!=0 and ot:
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pp=(p-op)/op if pos==1 else (op-p)/op
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hsec=(dt-ot).total_seconds()
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if -pp>=hsl: _close(p,dt); continue
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if hsec>=200:
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dc=False
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if -pp>=sl_pct: dc=True
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elif hsec>=mh: dc=True
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elif pos==1 and cd: dc=True
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elif pos==-1 and cu: dc=True
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elif pend=='cl' and pos==1: dc=True
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elif pend=='cs' and pos==-1: dc=True
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if dc:
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_close(p,dt)
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if atr_pct>=atr_min:
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if (cd or fast<slow) and p<big: _open('S',p,dt)
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elif (cu or fast>slow) and p>big: _open('L',p,dt)
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continue
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else:
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if pos==1 and cd: pend='cl'
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elif pos==-1 and cu: pend='cs'
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if pos==0 and atr_pct>=atr_min:
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if cu and p>big: _open('L',p,dt)
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elif cd and p<big: _open('S',p,dt)
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if pos!=0: _close(data[-1][4], data[-1][0])
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net=bal-1000.0; wr=wc/tc*100 if tc>0 else 0
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return net,tc,wr,dpnl,treb,tfee-treb
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def main():
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print("Loading...", flush=True)
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data = load()
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print(f"{len(data)} bars\n", flush=True)
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# ~50 targeted combos
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combos = []
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# Group 1: baseline variations (tweak one param at a time)
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base = (8, 21, 120, 0.0003, 0.004, 1800)
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# Vary ATR threshold (most impactful for reducing trades)
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for am in [0.0003, 0.0005, 0.0008, 0.001, 0.0013, 0.0015, 0.002, 0.003]:
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combos.append((8, 21, 120, am, 0.004, 1800))
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combos.append((8, 21, 120, am, 0.006, 1800))
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combos.append((8, 21, 120, am, 0.008, 1800))
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# Vary EMA periods
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for fp, sp in [(13, 34), (13, 55), (20, 55), (20, 80), (30, 80)]:
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for am in [0.0005, 0.001, 0.0015, 0.002]:
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combos.append((fp, sp, 120, am, 0.005, 1800))
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combos.append((fp, sp, 200, am, 0.005, 1800))
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combos.append((fp, sp, 120, am, 0.008, 3600))
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combos.append((fp, sp, 200, am, 0.008, 3600))
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# Vary max hold
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for mh in [900, 1800, 3600, 5400, 7200]:
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combos.append((8, 21, 120, 0.001, 0.005, mh))
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combos.append((13, 34, 120, 0.001, 0.005, mh))
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combos.append((13, 34, 200, 0.001, 0.008, mh))
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# Remove duplicates
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combos = list(set(combos))
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combos.sort()
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print(f"Combos: {len(combos)}\n", flush=True)
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results = []
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t0 = time.time()
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for idx, (fp, sp, bp, am, sl, mh) in enumerate(combos):
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net, tc, wr, dp, reb, nf = bt(data, fp, sp, bp, am, sl, mh)
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results.append((net, tc, wr, dp, reb, nf, fp, sp, bp, am, sl, mh))
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if (idx+1) % 10 == 0:
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el=time.time()-t0; eta=el/(idx+1)*(len(combos)-idx-1)
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print(f" [{idx+1}/{len(combos)}] {el:.0f}s / ~{eta:.0f}s left", flush=True)
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tt = time.time() - t0
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results.sort(key=lambda x: x[0], reverse=True)
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profitable = [r for r in results if r[0] > 0]
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print(f"\nDone! {tt:.1f}s | Profitable: {len(profitable)}/{len(results)}\n", flush=True)
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print("="*125, flush=True)
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print(" TOP 30 RESULTS (sorted by Net P&L)", flush=True)
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print("="*125, flush=True)
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print(f" {'#':>3} {'F':>3} {'S':>3} {'B':>4} {'ATR':>6} {'SL':>5} {'MH':>5} | {'Net%':>7} {'Net$':>9} {'#Trd':>6} {'WR':>6} {'DirPnL':>9} {'Rebate':>9} {'NetFee':>8}", flush=True)
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print(f" {'-'*119}", flush=True)
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for i,(net,tc,wr,dp,reb,nf,fp,sp,bp,am,sl,mh) in enumerate(results[:30]):
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mk=" <<<" if net>0 else ""
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print(f" {i+1:>3} {fp:>3} {sp:>3} {bp:>4} {am*100:>5.2f}% {sl*100:>4.1f}% {mh:>5} | {net/10:>+6.2f}% {net:>+8.2f} {tc:>6} {wr:>5.1f}% {dp:>+8.2f} {reb:>8.2f} {nf:>8.2f}{mk}", flush=True)
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if profitable:
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print(f"\n{'='*125}", flush=True)
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print(f" ALL {len(profitable)} PROFITABLE COMBOS", flush=True)
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print(f"{'='*125}", flush=True)
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for i,(net,tc,wr,dp,reb,nf,fp,sp,bp,am,sl,mh) in enumerate(profitable):
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print(f" {i+1:>3} EMA({fp}/{sp}/{bp}) ATR>{am*100:.2f}% SL={sl*100:.1f}% MH={mh}s | net={net:+.2f}$ ({net/10:+.2f}%) trades={tc} WR={wr:.1f}% dir={dp:+.2f} reb={reb:.2f} fee={nf:.2f}", flush=True)
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else:
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print("\n No profitable combos found. The EMA trend strategy may need a fundamentally different approach.", flush=True)
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# Show the closest to profitable
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print(f"\n Closest to breakeven:", flush=True)
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for i,(net,tc,wr,dp,reb,nf,fp,sp,bp,am,sl,mh) in enumerate(results[:5]):
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gap = -net
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print(f" EMA({fp}/{sp}/{bp}) ATR>{am*100:.2f}% SL={sl*100:.1f}% MH={mh}s | net={net:+.2f}$ gap_to_profit={gap:.2f}$ trades={tc} dir={dp:+.2f}", flush=True)
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print("="*125, flush=True)
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csv = Path(__file__).parent.parent / 'param_results.csv'
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with open(csv, 'w', encoding='utf-8-sig') as f:
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f.write("fast,slow,big,atr_min,stop_loss,max_hold,net_pct,net_usd,trades,win_rate,dir_pnl,rebate,net_fee\n")
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for net,tc,wr,dp,reb,nf,fp,sp,bp,am,sl,mh in results:
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f.write(f"{fp},{sp},{bp},{am},{sl},{mh},{net/10:.4f},{net:.4f},{tc},{wr:.2f},{dp:.4f},{reb:.4f},{nf:.4f}\n")
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print(f"\nSaved: {csv}", flush=True)
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if __name__=='__main__':
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main()
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