1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
|
"""Market on Close (MOC) Strategy — US Stock 종가매매.
Rules:
- Buy: 15:50-16:00 ET (market close) when screening criteria met
- Sell: 9:35-10:00 ET (market open next day)
- Screening: bullish candle, volume above average, RSI 30-60, positive momentum
- Risk: -2% stop loss, max 5 positions, 20% of capital per position
"""
from collections import deque
from datetime import datetime
from decimal import Decimal
import pandas as pd
from shared.models import Candle, OrderSide, Signal
from strategies.base import BaseStrategy
class MocStrategy(BaseStrategy):
"""Market on Close strategy for overnight gap trading."""
name: str = "moc"
def __init__(self) -> None:
super().__init__()
# Parameters
self._quantity_pct: float = 0.2 # 20% of capital per trade
self._stop_loss_pct: float = 2.0
self._rsi_min: float = 30.0
self._rsi_max: float = 60.0
self._ema_period: int = 20
self._volume_avg_period: int = 20
self._min_volume_ratio: float = 1.0 # Volume must be above average
# Session times (UTC hours)
self._buy_start_utc: int = 19 # 15:00 ET = 19:00 UTC (summer) / 20:00 UTC (winter)
self._buy_end_utc: int = 21 # 16:00 ET = 20:00 UTC / 21:00 UTC
self._sell_start_utc: int = 13 # 9:00 ET = 13:00 UTC / 14:00 UTC
self._sell_end_utc: int = 15 # 10:00 ET = 14:00 UTC / 15:00 UTC
self._max_positions: int = 5
# State
self._closes: deque[float] = deque(maxlen=200)
self._volumes: deque[float] = deque(maxlen=200)
self._highs: deque[float] = deque(maxlen=200)
self._lows: deque[float] = deque(maxlen=200)
self._in_position: bool = False
self._entry_price: float = 0.0
self._today: str | None = None
self._bought_today: bool = False
self._sold_today: bool = False
@property
def warmup_period(self) -> int:
return max(self._ema_period, self._volume_avg_period) + 1
def configure(self, params: dict) -> None:
self._quantity_pct = float(params.get("quantity_pct", 0.2))
self._stop_loss_pct = float(params.get("stop_loss_pct", 2.0))
self._rsi_min = float(params.get("rsi_min", 30.0))
self._rsi_max = float(params.get("rsi_max", 60.0))
self._ema_period = int(params.get("ema_period", 20))
self._volume_avg_period = int(params.get("volume_avg_period", 20))
self._min_volume_ratio = float(params.get("min_volume_ratio", 1.0))
self._buy_start_utc = int(params.get("buy_start_utc", 19))
self._buy_end_utc = int(params.get("buy_end_utc", 21))
self._sell_start_utc = int(params.get("sell_start_utc", 13))
self._sell_end_utc = int(params.get("sell_end_utc", 15))
self._max_positions = int(params.get("max_positions", 5))
if self._quantity_pct <= 0 or self._quantity_pct > 1:
raise ValueError(f"quantity_pct must be 0-1, got {self._quantity_pct}")
if self._stop_loss_pct <= 0:
raise ValueError(f"stop_loss_pct must be positive, got {self._stop_loss_pct}")
def reset(self) -> None:
super().reset()
self._closes.clear()
self._volumes.clear()
self._highs.clear()
self._lows.clear()
self._in_position = False
self._entry_price = 0.0
self._today = None
self._bought_today = False
self._sold_today = False
def _is_buy_window(self, dt: datetime) -> bool:
"""Check if in buy window (near market close)."""
hour = dt.hour
return self._buy_start_utc <= hour < self._buy_end_utc
def _is_sell_window(self, dt: datetime) -> bool:
"""Check if in sell window (near market open)."""
hour = dt.hour
return self._sell_start_utc <= hour < self._sell_end_utc
def _compute_rsi(self, period: int = 14) -> float | None:
if len(self._closes) < period + 1:
return None
series = pd.Series(list(self._closes))
delta = series.diff()
gain = delta.clip(lower=0)
loss = -delta.clip(upper=0)
avg_gain = gain.ewm(com=period - 1, min_periods=period).mean()
avg_loss = loss.ewm(com=period - 1, min_periods=period).mean()
rs = avg_gain / avg_loss.replace(0, float("nan"))
rsi = 100 - (100 / (1 + rs))
val = rsi.iloc[-1]
return None if pd.isna(val) else float(val)
def _is_bullish_candle(self, candle: Candle) -> bool:
return float(candle.close) > float(candle.open)
def _price_above_ema(self) -> bool:
if len(self._closes) < self._ema_period:
return True
series = pd.Series(list(self._closes))
ema = series.ewm(span=self._ema_period, adjust=False).mean().iloc[-1]
return self._closes[-1] >= ema
def _volume_above_average(self) -> bool:
if len(self._volumes) < self._volume_avg_period:
return True
avg = sum(list(self._volumes)[-self._volume_avg_period :]) / self._volume_avg_period
return avg > 0 and self._volumes[-1] / avg >= self._min_volume_ratio
def _positive_momentum(self) -> bool:
"""Check if price has positive short-term momentum (close > close 5 bars ago)."""
if len(self._closes) < 6:
return True
return self._closes[-1] > self._closes[-6]
def on_candle(self, candle: Candle) -> Signal | None:
self._update_filter_data(candle)
close = float(candle.close)
self._closes.append(close)
self._volumes.append(float(candle.volume))
self._highs.append(float(candle.high))
self._lows.append(float(candle.low))
# Daily reset
day = candle.open_time.strftime("%Y-%m-%d")
if self._today != day:
self._today = day
self._bought_today = False
self._sold_today = False
# --- SELL LOGIC (market open next day) ---
if self._in_position and self._is_sell_window(candle.open_time):
if not self._sold_today:
pnl_pct = (close - self._entry_price) / self._entry_price * 100
self._in_position = False
self._sold_today = True
conv = 0.8 if pnl_pct > 0 else 0.5
return self._apply_filters(
Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.SELL,
price=candle.close,
quantity=Decimal(str(self._quantity_pct)),
conviction=conv,
reason=f"MOC sell at open, PnL {pnl_pct:.2f}%",
)
)
# --- STOP LOSS ---
if self._in_position:
pnl_pct = (close - self._entry_price) / self._entry_price * 100
if pnl_pct <= -self._stop_loss_pct:
self._in_position = False
return self._apply_filters(
Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.SELL,
price=candle.close,
quantity=Decimal(str(self._quantity_pct)),
conviction=1.0,
stop_loss=candle.close,
reason=f"MOC stop loss {pnl_pct:.2f}% <= -{self._stop_loss_pct}%",
)
)
# --- BUY LOGIC (near market close) ---
if not self._in_position and self._is_buy_window(candle.open_time):
if self._bought_today:
return None
# Screening criteria
rsi = self._compute_rsi()
if rsi is None:
return None
checks = [
self._rsi_min <= rsi <= self._rsi_max, # RSI in sweet spot
self._is_bullish_candle(candle), # Bullish candle
self._price_above_ema(), # Above EMA (uptrend)
self._volume_above_average(), # Volume confirmation
self._positive_momentum(), # Short-term momentum
]
if all(checks):
self._in_position = True
self._entry_price = close
self._bought_today = True
# Conviction based on RSI position within range
rsi_range = self._rsi_max - self._rsi_min
rsi_pos = (rsi - self._rsi_min) / rsi_range if rsi_range > 0 else 0.5
conv = 0.5 + (1.0 - rsi_pos) * 0.4 # Lower RSI = higher conviction
sl = candle.close * (1 - Decimal(str(self._stop_loss_pct / 100)))
return self._apply_filters(
Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.BUY,
price=candle.close,
quantity=Decimal(str(self._quantity_pct)),
conviction=conv,
stop_loss=sl,
reason=f"MOC buy: RSI={rsi:.1f}, bullish candle, above EMA, vol OK",
)
)
return None
|
