import pandas as pd import yfinance as yf import plotly.graph_objs as go from plotly import subplots import plotly.io as pio from datetime import datetime pio.renderers.default = 'browser' from config import * from monitor_min import Monitor class Simulation: def render_plotly(self, symbol: str, interval_minutes: int, data: pd.DataFrame, inverseData: pd.DataFrame) -> None: fig = subplots.make_subplots( rows=3, cols=1, subplot_titles=("캔들", "이격도/거래량", "장기 이격도"), shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.03, row_heights=[0.6, 0.2, 0.2] ) # Row 1: 캔들 + 이동평균 + 볼린저 fig.add_trace(go.Candlestick(x=data.index, open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='캔들'), row=1, col=1) for ma_col, color in [('MA5','red'),('MA20','blue'),('MA40','green'),('MA120','purple'),('MA200','brown'),('MA240','darkred'),('MA720','cyan'),('MA1440','magenta')]: if ma_col in data.columns: fig.add_trace(go.Scatter(x=data.index, y=data[ma_col], name=ma_col, mode='lines', line=dict(color=color, width=1)), row=1, col=1) if 'Lower' in data.columns and 'Upper' in data.columns: fig.add_trace(go.Scatter(x=data.index, y=data['Lower'], name='볼린저 하단', mode='lines', line=dict(color='grey', width=1, dash='dot')), row=1, col=1) fig.add_trace(go.Scatter(x=data.index, y=data['Upper'], name='볼린저 상단', mode='lines', line=dict(color='grey', width=1, dash='dot')), row=1, col=1) # 매수 포인트 for sig, color in [('movingaverage','red'),('deviation40','orange'),('Deviation720','blue'),('deviation1440','purple'),('fall_6p','black')]: pts = data[(data['point']==1) & (data['signal']==sig)] if len(pts)>0: fig.add_trace(go.Scatter(x=pts.index, y=pts['Close'], mode='markers', name=f'{sig} 매수', marker=dict(color=color, size=8, symbol='circle')), row=1, col=1) # 매도 포인트: inverseData의 buy 신호 중 fall_6p, deviation40만 일반 그래프 가격축에 매도로 표시 inv_sell_pts = inverseData[(inverseData['point']==1) & (inverseData['signal'].isin(['deviation40','fall_6p']))] if len(inv_sell_pts)>0: idx = inv_sell_pts.index.intersection(data.index) if len(idx)>0: fig.add_trace( go.Scatter( x=idx, y=data.loc[idx, 'Close'], mode='markers', name='매도', marker=dict(color='orange', size=10, symbol='triangle-down') ), row=1, col=1 ) # Row 2: 이격도 + 거래량 for dev_col, color, width in [('Deviation5','red',1),('Deviation20','blue',1),('Deviation40','green',2),('Deviation120','purple',1),('Deviation200','brown',1),('Deviation720','darkred',2),('Deviation720','cyan',1),('Deviation1440','magenta',1)]: if dev_col in data.columns: fig.add_trace(go.Scatter(x=data.index, y=data[dev_col], name=dev_col, mode='lines', line=dict(color=color, width=width)), row=2, col=1) if 'Volume' in data.columns: fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='거래량', marker_color='lightgray', opacity=0.5), row=2, col=1) # Row 3: 장기 이격도 및 기준선 for dev_col, color in [('Deviation720','darkred'),('Deviation1440','magenta')]: if dev_col in data.columns: fig.add_trace(go.Scatter(x=data.index, y=data[dev_col], name=f'{dev_col}(장기)', mode='lines', line=dict(color=color, width=2)), row=3, col=1) for h, color in [(90,'red'),(95,'green'),(100,'black')]: fig.add_hline(y=h, line_width=1, line_dash='dash', line_color=color, row=3, col=1) # ----------------- 인버스용 트레이스 (초기 숨김) ----------------- n_orig = len(fig.data) # Row 1: 캔들/MA/볼린저 (inverseData) fig.add_trace(go.Candlestick(x=inverseData.index, open=inverseData['Open'], high=inverseData['High'], low=inverseData['Low'], close=inverseData['Close'], name='캔들(인버스)', showlegend=True, visible=False), row=1, col=1) for ma_col, color in [('MA5','red'),('MA20','blue'),('MA40','green'),('MA120','purple'),('MA200','brown'),('MA240','darkred'),('MA720','cyan'),('MA1440','magenta')]: if ma_col in inverseData.columns: fig.add_trace(go.Scatter(x=inverseData.index, y=inverseData[ma_col], name=f'{ma_col}(인버스)', mode='lines', line=dict(color=color, width=1), showlegend=True, visible=False), row=1, col=1) if 'Lower' in inverseData.columns and 'Upper' in inverseData.columns: fig.add_trace(go.Scatter(x=inverseData.index, y=inverseData['Lower'], name='볼린저 하단(인버스)', mode='lines', line=dict(color='grey', width=1, dash='dot'), showlegend=True, visible=False), row=1, col=1) fig.add_trace(go.Scatter(x=inverseData.index, y=inverseData['Upper'], name='볼린저 상단(인버스)', mode='lines', line=dict(color='grey', width=1, dash='dot'), showlegend=True, visible=False), row=1, col=1) # 인버스 매수 포인트: fall_6p, deviation40만 표시 for sig, color in [('deviation40','orange'),('fall_6p','black')]: pts_inv = inverseData[(inverseData['point']==1) & (inverseData['signal']==sig)] if len(pts_inv)>0: fig.add_trace(go.Scatter(x=pts_inv.index, y=inverseData.loc[pts_inv.index,'Close'], mode='markers', name=f'{sig} 매수(인버스)', marker=dict(color=color, size=8, symbol='circle'), showlegend=True, visible=False), row=1, col=1) # 인버스 보기에서의 매도 포인트: 일반 그래프의 매수를 인버스 그래프의 매도로 표시 (모든 매수 신호 반영) normal_to_inv_sell = data[(data['point']==1)] if len(normal_to_inv_sell) > 0: idx2 = normal_to_inv_sell.index.intersection(inverseData.index) if len(idx2) > 0: fig.add_trace( go.Scatter( x=idx2, y=inverseData.loc[idx2, 'Close'], mode='markers', name='매도(일반→인버스)', marker=dict(color='orange', size=10, symbol='triangle-down'), showlegend=True, visible=False ), row=1, col=1 ) # Row 2: 이격도 + 거래량 (inverseData) for dev_col, color, width in [('Deviation5','red',1),('Deviation20','blue',1),('Deviation40','green',2),('Deviation120','purple',1),('Deviation200','brown',1),('Deviation720','darkred',2),('Deviation720','cyan',1),('Deviation1440','magenta',1)]: if dev_col in inverseData.columns: fig.add_trace(go.Scatter(x=inverseData.index, y=inverseData[dev_col], name=f'{dev_col}(인버스)', mode='lines', line=dict(color=color, width=width), showlegend=True, visible=False), row=2, col=1) if 'Volume' in inverseData.columns: fig.add_trace(go.Bar(x=inverseData.index, y=inverseData['Volume'], name='거래량(인버스)', marker_color='lightgray', opacity=0.5, showlegend=True, visible=False), row=2, col=1) # Row 3: 장기 이격도 (inverseData) for dev_col, color in [('Deviation720','darkred'),('Deviation1440','magenta')]: if dev_col in inverseData.columns: fig.add_trace(go.Scatter(x=inverseData.index, y=inverseData[dev_col], name=f'{dev_col}(장기-인버스)', mode='lines', line=dict(color=color, width=2), showlegend=True, visible=False), row=3, col=1) n_total = len(fig.data) n_inv = n_total - n_orig visible_orig = [True]*n_orig + [False]*n_inv visible_inv = [False]*n_orig + [True]*n_inv legendtitle_orig = {'text': '일반 그래프'} legendtitle_inv = {'text': '인버스 그래프'} fig.update_layout( height=1000, margin=dict(t=180, l=40, r=240, b=40), title=dict( text=f"{symbol}, {interval_minutes} 분봉, ({datetime.now().strftime('%Y-%m-%d %H:%M:%S')})", x=0.5, xanchor='center', y=0.995, yanchor='top', pad=dict(t=10, b=12) ), xaxis_rangeslider_visible=False, xaxis1_rangeslider_visible=False, xaxis2_rangeslider_visible=False, legend=dict(orientation='v', yref='paper', yanchor='top', y=1.0, xref='paper', xanchor='left', x=1.02, title=legendtitle_orig), dragmode='zoom', updatemenus=[dict( type='buttons', direction='left', x=0.0, xanchor='left', y=1.11, yanchor='top', pad=dict(t=0, r=10, b=0, l=0), buttons=[ dict( label='홈', method='update', args=[ {'visible': visible_orig}, { 'legend': {'title': legendtitle_orig}, 'xaxis.autorange': True, 'xaxis2.autorange': True, 'xaxis3.autorange': True, 'yaxis.autorange': True, 'yaxis2.autorange': True, 'yaxis3.autorange': True, } ], execute=True ), dict( label='인버스', method='update', args=[ {'visible': visible_inv}, {'legend': {'title': legendtitle_inv, 'orientation': 'v', 'y': 1.0, 'yanchor': 'top', 'x': 1.02, 'xanchor': 'left'}} ], args2=[ {'visible': visible_orig}, {'legend': {'title': legendtitle_orig, 'orientation': 'v', 'y': 1.0, 'yanchor': 'top', 'x': 1.02, 'xanchor': 'left'}} ], execute=True ), ] )] ) fig.update_xaxes(title_text='시간', row=3, col=1) fig.update_yaxes(title_text='가격 (KRW)', row=1, col=1) fig.update_yaxes(title_text='이격도/거래량', row=2, col=1) fig.update_yaxes(title_text='장기 이격도', row=3, col=1) fig.show(config={'scrollZoom': True, 'displaylogo': False}) def __init__(self) -> None: self.monitor = Monitor() self.INTERVAL_MAP = { 60: "60m", 240: "4h", } def detect_turnaround_signal(self, symbol, data, interval=0, params=None): if len(data) < 7: return None current_data = data.iloc[-1] if current_data.get('point', 0) == 1: return { 'alert': True, 'details': f"매수신호: {current_data.get('signal', 'unknown')}" } return {'alert': False, 'details': "매수신호 없음"} def fetch_price_history(self, symbol: str, interval_minutes: int, days: int = 30) -> pd.DataFrame: if symbol in KR_COINS: bong_count = 3000 return self.monitor.get_coin_more_data(symbol, interval_minutes, bong_count=bong_count) if interval_minutes not in self.INTERVAL_MAP: raise ValueError("interval must be 60 or 240") interval_str = self.INTERVAL_MAP[interval_minutes] df = yf.download( tickers=symbol, period=f"{days}d", interval=interval_str, progress=False, ) if df.empty: raise RuntimeError("No data fetched. Check symbol or interval support.") return df def analyze_bottom_period(self, symbol: str, interval_minutes: int, days: int = 90): data = self.fetch_price_history(symbol, interval_minutes, days) data = self.monitor.calculate_technical_indicators(data) data = self.monitor.annotate_signals(symbol, data, simulation=True) print(f"데이터 기간: {data.index[0]} ~ {data.index[-1]}") print(f"총 데이터 수: {len(data)}") bottom_start = pd.Timestamp('2025-06-22') bottom_end = pd.Timestamp('2025-07-09') bottom_data = data[(data.index >= bottom_start) & (data.index <= bottom_end)] if len(bottom_data) == 0: print("저점 기간 데이터가 없습니다.") return None, [] print(f"\n저점 기간 데이터: {bottom_data.index[0]} ~ {bottom_data.index[-1]}") print(f"저점 기간 데이터 수: {len(bottom_data)}") print("\n=== 저점 기간 기술적 지표 분석 ===") min_price = bottom_data['Low'].min() max_price = bottom_data['High'].max() avg_price = bottom_data['Close'].mean() print(f"최저가: {min_price:.4f}") print(f"최고가: {max_price:.4f}") print(f"평균가: {avg_price:.4f}") print(f"가격 변동폭: {((max_price - min_price) / min_price * 100):.2f}%") bb_lower_min = bottom_data['Lower'].min() bb_upper_max = bottom_data['Upper'].max() print(f"\n볼린저 밴드 분석:") print(f"하단 밴드 최저: {bb_lower_min:.4f}") print(f"상단 밴드 최고: {bb_upper_max:.4f}") volume_avg = bottom_data['Volume'].mean() volume_max = bottom_data['Volume'].max() print(f"\n거래량 분석:") print(f"평균 거래량: {volume_avg:.0f}") print(f"최대 거래량: {volume_max:.0f}") actual_bottom_idx = bottom_data['Low'].idxmin() actual_bottom_price = bottom_data.loc[actual_bottom_idx, 'Low'] actual_bottom_date = actual_bottom_idx print(f"\n실제 저점:") print(f"날짜: {actual_bottom_date}") print(f"가격: {actual_bottom_price:.4f}") print(f"볼린저 하단 대비: {((actual_bottom_price - bottom_data.loc[actual_bottom_idx, 'Lower']) / bottom_data.loc[actual_bottom_idx, 'Lower'] * 100):.2f}%") print(f"\n=== 매수 신호 분석 ===") bottom_alerts = bottom_data[bottom_data['point'] == 1] alerts = [(idx, row['Close']) for idx, row in bottom_alerts.iterrows()] print(f"저점 기간 매수 신호 수: {len(alerts)}") if alerts: print("매수 신호 발생 시점:") for date, price in alerts: print(f" {date}: {price:.4f}") return bottom_data, alerts def run_simulation(self, symbol: str, interval_minutes: int, days: int = 30): data = self.fetch_price_history(symbol, interval_minutes) inverseData = self.monitor.inverse_data(data) inverseData = self.monitor.annotate_signals(symbol, inverseData, simulation=True) data = self.monitor.calculate_technical_indicators(data) data = self.monitor.annotate_signals(symbol, data, simulation=True) print(f"데이터 기간: {data.index[0]} ~ {data.index[-1]}") print(f"총 데이터 수: {len(data)}") alerts = [] for i in range(len(data)): if data['point'].iloc[i] == 1: alerts.append((data.index[i], data['Close'].iloc[i])) print(f"\n총 매수 신호 수: {len(alerts)}") ma_signals = len(data[(data['point'] == 1) & (data['signal'] == 'movingaverage')]) dev40_signals = len(data[(data['point'] == 1) & (data['signal'] == 'deviation40')]) dev240_signals = len(data[(data['point'] == 1) & (data['signal'] == 'Deviation720')]) dev1440_signals = len(data[(data['point'] == 1) & (data['signal'] == 'deviation1440')]) print(f" - MA 신호: {ma_signals}") print(f" - Dev40 신호: {dev40_signals}") print(f" - Dev240 신호: {dev240_signals}") print(f" - Dev1440 신호: {dev1440_signals}") # Plotly 기반 시각화로 전환 self.render_plotly(symbol, interval_minutes, data, inverseData) return if __name__ == "__main__": sim = Simulation() interval = 60 days = 90 target_coins = ['XRP'] show_graphs = True for symbol in target_coins: print(f"\n=== {symbol} 저점 기간 분석 시작 ===") try: bottom_data, alerts = sim.analyze_bottom_period(symbol, interval, days) print(f"\n=== {symbol} 전체 기간 시뮬레이션 ===") if show_graphs: sim.run_simulation(symbol, interval, days) else: data = sim.fetch_price_history(symbol, interval, days) inverseData = sim.monitor.inverse_data(data) inverseData = sim.monitor.annotate_signals(symbol, inverseData, simulation=True) data = sim.monitor.calculate_technical_indicators(data) data = sim.monitor.annotate_signals(symbol, data, simulation=True) total_signals = len(data[data['point'] == 1]) ma_signals = len(data[(data['point'] == 1) & (data['signal'] == 'movingaverage')]) dev40_signals = len(data[(data['point'] == 1) & (data['signal'] == 'deviation40')]) dev240_signals = len(data[(data['point'] == 1) & (data['signal'] == 'Deviation720')]) dev1440_signals = len(data[(data['point'] == 1) & (data['signal'] == 'deviation1440')]) print(f"총 매수 신호: {total_signals}") print(f" - MA 신호: {ma_signals}") print(f" - Dev40 신호: {dev40_signals}") print(f" - Dev240 신호: {dev240_signals}") print(f" - Dev1440 신호: {dev1440_signals}") except Exception as e: print(f"Error analyzing {symbol}: {str(e)}")