290 lines
9.1 KiB
Python
290 lines
9.1 KiB
Python
"""
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EMA趋势策略 - 快速参数优化（精简版）
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前次发现：EMA(8/21/120) 方向盈利 +327，但10%费用成本 428，净亏 -101。
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优化方向：用更长EMA减少交易次数 + 更高ATR过滤提高质量。
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"""
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import sys
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import time
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import datetime
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import sqlite3
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from pathlib import Path
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class EMA:
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def __init__(self, period):
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self.k = 2.0 / (period + 1)
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self.value = None
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def update(self, price):
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if self.value is None:
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self.value = price
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else:
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self.value = price * self.k + self.value * (1 - self.k)
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return self.value
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def load_data():
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db_path = Path(__file__).parent.parent / 'models' / 'database.db'
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start_ms = int(datetime.datetime(2025, 1, 1).timestamp()) * 1000
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end_ms = int(datetime.datetime(2026, 1, 1).timestamp()) * 1000
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conn = sqlite3.connect(str(db_path))
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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",
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(start_ms, end_ms)
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).fetchall()
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conn.close()
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data = []
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for r in rows:
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data.append((
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datetime.datetime.fromtimestamp(r[0] / 1000.0),
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r[1], r[2], r[3], r[4], # open, high, low, close
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))
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return data
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def backtest(data, fp, sp, bp, atr_min, sl_pct, mh):
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bal = 1000.0
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pos = 0
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op = 0.0
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ot = None
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ps = 0.0
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pending = None
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ef = EMA(fp)
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es = EMA(sp)
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eb = EMA(bp)
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highs = []
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lows = []
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closes = []
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pf = None
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pslow = None
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tc = 0
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wc = 0
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dir_pnl = 0.0
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tot_fee = 0.0
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tot_reb = 0.0
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hsl = sl_pct * 1.5
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lev = 50
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rp = 0.005
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tf_rate = 0.0006
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reb_rate = 0.90
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min_h = 200
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for dt, o_, h_, l_, c_ in data:
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price = c_
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highs.append(h_)
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lows.append(l_)
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closes.append(price)
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fast = ef.update(price)
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slow = es.update(price)
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big = eb.update(price)
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# ATR
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atr_pct = 0
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ap = 14
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if len(highs) > ap + 1:
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trs = []
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for i in range(-ap, 0):
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tr = max(highs[i] - lows[i], abs(highs[i] - closes[i-1]), abs(lows[i] - closes[i-1]))
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trs.append(tr)
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atr_pct = (sum(trs) / ap) / price if price > 0 else 0
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cu = pf is not None and pf <= pslow and fast > slow
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cd = pf is not None and pf >= pslow and fast < slow
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pf = fast
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pslow = slow
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if pos != 0 and ot:
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if pos == 1:
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p = (price - op) / op
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else:
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p = (op - price) / op
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hs = (dt - ot).total_seconds()
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if -p >= hsl:
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# close
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pnl_ = ps * p
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cv = ps * (1 + p)
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cf = cv * tf_rate
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of_ = ps * tf_rate
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ttf = of_ + cf
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rb = ttf * reb_rate
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bal += pnl_ - cf + rb
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dir_pnl += pnl_
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tot_fee += ttf
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tot_reb += rb
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tc += 1
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if pnl_ > 0: wc += 1
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pos = 0; op = 0; ot = None; ps = 0; pending = None
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continue
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can_c = hs >= min_h
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if can_c:
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do_close = False
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if -p >= sl_pct:
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do_close = True
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elif hs >= mh:
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do_close = True
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elif pos == 1 and cd:
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do_close = True
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elif pos == -1 and cu:
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do_close = True
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elif pending == 'cl' and pos == 1:
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do_close = True
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elif pending == 'cs' and pos == -1:
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do_close = True
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if do_close:
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pnl_ = ps * p
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cv = ps * (1 + p)
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cf = cv * tf_rate
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of_ = ps * tf_rate
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ttf = of_ + cf
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rb = ttf * reb_rate
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bal += pnl_ - cf + rb
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dir_pnl += pnl_
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tot_fee += ttf
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tot_reb += rb
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tc += 1
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if pnl_ > 0: wc += 1
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pos = 0; op = 0; ot = None; ps = 0; pending = None
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# re-enter
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if atr_pct >= atr_min:
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if (cd or (fast < slow)) and price < big:
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ns = bal * rp * lev
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if ns >= 1:
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bal -= ns * tf_rate
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pos = -1; op = price; ot = dt; ps = ns
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elif (cu or (fast > slow)) and price > big:
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ns = bal * rp * lev
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if ns >= 1:
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bal -= ns * tf_rate
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pos = 1; op = price; ot = dt; ps = ns
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continue
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else:
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if pos == 1 and cd: pending = 'cl'
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elif pos == -1 and cu: pending = 'cs'
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if pos == 0 and atr_pct >= atr_min:
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if cu and price > big:
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ns = bal * rp * lev
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if ns >= 1:
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bal -= ns * tf_rate
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pos = 1; op = price; ot = dt; ps = ns
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elif cd and price < big:
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ns = bal * rp * lev
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if ns >= 1:
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bal -= ns * tf_rate
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pos = -1; op = price; ot = dt; ps = ns
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# force close
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if pos != 0:
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price = data[-1][4]
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if pos == 1:
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p = (price - op) / op
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else:
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p = (op - price) / op
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pnl_ = ps * p
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cv = ps * (1 + p)
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cf = cv * tf_rate
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of_ = ps * tf_rate
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ttf = of_ + cf
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rb = ttf * reb_rate
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bal += pnl_ - cf + rb
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dir_pnl += pnl_
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tot_fee += ttf
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tot_reb += rb
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tc += 1
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if pnl_ > 0: wc += 1
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net = bal - 1000.0
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wr = wc / tc * 100 if tc > 0 else 0
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return net, tc, wr, dir_pnl, tot_reb, tot_fee - tot_reb
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def main():
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print("Loading data...", flush=True)
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data = load_data()
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print(f"Loaded {len(data)} bars", flush=True)
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# Focused parameter grid (smaller)
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fast_list = [8, 13, 20]
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slow_list = [21, 34, 55]
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big_list = [120, 200]
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atr_list = [0.0003, 0.0006, 0.001, 0.0015]
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sl_list = [0.003, 0.005, 0.008]
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mh_list = [900, 1800, 3600]
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combos = []
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for fp in fast_list:
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for sp in slow_list:
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if fp >= sp: continue
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for bp in big_list:
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for am in atr_list:
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for sl in sl_list:
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for mh in mh_list:
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combos.append((fp, sp, bp, am, sl, mh))
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print(f"\nTotal combos: {len(combos)}", 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 = backtest(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) % 20 == 0:
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elapsed = time.time() - t0
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eta = elapsed / (idx + 1) * (len(combos) - idx - 1)
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print(f" [{idx+1}/{len(combos)}] {elapsed:.0f}s elapsed, ~{eta:.0f}s remaining", flush=True)
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total_time = time.time() - t0
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print(f"\nDone! {len(results)} combos in {total_time:.1f}s\n", flush=True)
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# Sort by net P&L
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results.sort(key=lambda x: x[0], reverse=True)
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# Print results
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profitable = [r for r in results if r[0] > 0]
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print(f"Profitable: {len(profitable)} / {len(results)} ({len(profitable)/len(results)*100:.1f}%)\n")
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print(f"{'='*130}")
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print(f" TOP 30 RESULTS")
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print(f"{'='*130}")
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header = f" {'#':>3} {'Fast':>4} {'Slow':>4} {'Big':>4} {'ATR%':>6} {'SL%':>5} {'MaxH':>5} | {'Net%':>7} {'Net$':>9} {'Trades':>7} {'WR':>6} {'DirPnL':>9} {'Rebate':>9} {'NetFee':>8}"
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print(header)
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print(f" {'-'*126}")
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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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net_pct = net / 10 # net / 1000 * 100
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marker = " ***" if net > 0 else ""
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print(f" {i+1:>3} {fp:>4} {sp:>4} {bp:>4} {am*100:>5.2f}% {sl*100:>4.1f}% {mh:>5} | {net_pct:>+6.2f}% {net:>+8.2f} {tc:>7} {wr:>5.1f}% {dp:>+8.2f} {reb:>8.2f} {nf:>8.2f}{marker}")
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print(f"{'='*130}")
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if profitable:
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print(f"\nALL PROFITABLE:")
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print(f" {'-'*126}")
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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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net_pct = net / 10
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print(f" {i+1:>3} EMA({fp}/{sp}/{bp}) ATR>{am*100:.2f}% SL={sl*100:.1f}% MaxH={mh}s | {net_pct:>+6.2f}% {net:>+8.2f} trades={tc} WR={wr:.1f}% DirPnL={dp:+.2f} Rebate={reb:.2f}")
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# Save CSV
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csv_path = Path(__file__).parent.parent / 'param_results.csv'
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with open(csv_path, '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"\nResults saved: {csv_path}")
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if __name__ == '__main__':
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main()
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