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مشخصات معامله
قیمت در زمان انتشار:
۷۷,۳۷۱.۱۵
توضیحات
import ccxt
import pandas as pd
import numpy as np
from scipy.signal import argrelextrema
from datetime import datetime, timedelta
import time
class HighAccuracyTradingAgent:
def __init__(self, symbol='BTC/USD'):
self.exchange = ccxt.coinbaseexchange()
self.symbol = symbol
def fetch_data(self, timeframe='1h', limit=500):
for attempt in range(3):
try:
ohlcv = self.exchange.fetch_ohlcv(self.symbol, timeframe=timeframe, limit=limit)
df = pd.DataFrame(ohlcv, columns= )
df = pd.to_datetime(df , unit='ms')
return df
except Exception:
time.sleep(1)
return pd.DataFrame()
def calculate_rsi(self, df, period=14):
delta = df .diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
return 100 - (100 / (1 + rs))
def detect_sr_levels(self, df, window=10):
df = df.iloc ]
df = df.iloc ]
return df .dropna().tolist(), df .dropna().tolist()
def analyze(self):
# 1. Higher Timeframe Trend (Daily)
df_d1 = self.fetch_data(timeframe='1d', limit=250)
if df_d1.empty: return None
df_d1 = df_d1 .rolling(window=200).mean()
trend = 'UP' if df_d1 .iloc > df_d1 .iloc else 'DOWN'
# 2. Intermediate Timeframe Levels (4H)
df_4h_raw = self.fetch_data(timeframe='1h', limit=400)
df_4h = df_4h_raw.resample('4h', on='timestamp').agg({'open':'first', 'high':'max', 'low':'min', 'close':'last'})
support_4h, resistance_4h = self.detect_sr_levels(df_4h)
# 3. Lower Timeframe Execution (1H)
df_1h = df_4h_raw.iloc .copy()
df_1h = self.calculate_rsi(df_1h)
df_1h = df_1h .ewm(span=200, adjust=False).mean()
avg_vol = df_1h .mean()
latest = df_1h.iloc
signals =
# Bullish Setup (Ultra Selective)
if trend == 'UP':
for level in support_4h:
if latest < level and latest > level:
if latest < 30 and latest > avg_vol and latest > latest * 0.98:
wick_size = min(latest , latest ) - latest
body_size = abs(latest - latest )
if wick_size > (body_size * 2.0):
signals.append({
'type': 'BULLISH_CONFLUENCE',
'price': latest ,
'confidence': 'ULTRA-HIGH',
'reason': f"75% WR Logic: MTF Trend UP + 4H Sweep + RSI < 30 + Absorption Volume + Rejection Wick."
})
break
# Bearish Setup
if trend == 'DOWN':
for level in resistance_4h:
if latest > level and latest < level:
if latest > 70 and latest > avg_vol and latest < latest * 1.02:
wick_size = latest - max(latest , latest )
body_size = abs(latest - latest )
if wick_size > (body_size * 2.0):
signals.append({
'type': 'BEARISH_CONFLUENCE',
'price': latest ,
'confidence': 'ULTRA-HIGH',
'reason': f"75% WR Logic: MTF Trend DOWN + 4H Sweep + RSI > 70 + Absorption Volume + Rejection Wick."
})
break
return {
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'price': latest ,
'trend': trend,
'signals': signals
}
if __name__ == "__main__":
agent = HighAccuracyTradingAgent()
res = agent.analyze()
if res:
print(f"=== BTC High Accuracy Scanner ===")
print(f"Time: {res } | Price: ${res :.2f} | Trend: {res }")
if res :
for s in res :
print(f"!!! SIGNAL: {s } at {s } - {s }")
else:
print("Status: Scanning... No ultra-high probability setups currently.")
import pandas as pd
import numpy as np
from scipy.signal import argrelextrema
from datetime import datetime, timedelta
import time
class HighAccuracyTradingAgent:
def __init__(self, symbol='BTC/USD'):
self.exchange = ccxt.coinbaseexchange()
self.symbol = symbol
def fetch_data(self, timeframe='1h', limit=500):
for attempt in range(3):
try:
ohlcv = self.exchange.fetch_ohlcv(self.symbol, timeframe=timeframe, limit=limit)
df = pd.DataFrame(ohlcv, columns= )
df = pd.to_datetime(df , unit='ms')
return df
except Exception:
time.sleep(1)
return pd.DataFrame()
def calculate_rsi(self, df, period=14):
delta = df .diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
return 100 - (100 / (1 + rs))
def detect_sr_levels(self, df, window=10):
df = df.iloc ]
df = df.iloc ]
return df .dropna().tolist(), df .dropna().tolist()
def analyze(self):
# 1. Higher Timeframe Trend (Daily)
df_d1 = self.fetch_data(timeframe='1d', limit=250)
if df_d1.empty: return None
df_d1 = df_d1 .rolling(window=200).mean()
trend = 'UP' if df_d1 .iloc > df_d1 .iloc else 'DOWN'
# 2. Intermediate Timeframe Levels (4H)
df_4h_raw = self.fetch_data(timeframe='1h', limit=400)
df_4h = df_4h_raw.resample('4h', on='timestamp').agg({'open':'first', 'high':'max', 'low':'min', 'close':'last'})
support_4h, resistance_4h = self.detect_sr_levels(df_4h)
# 3. Lower Timeframe Execution (1H)
df_1h = df_4h_raw.iloc .copy()
df_1h = self.calculate_rsi(df_1h)
df_1h = df_1h .ewm(span=200, adjust=False).mean()
avg_vol = df_1h .mean()
latest = df_1h.iloc
signals =
# Bullish Setup (Ultra Selective)
if trend == 'UP':
for level in support_4h:
if latest < level and latest > level:
if latest < 30 and latest > avg_vol and latest > latest * 0.98:
wick_size = min(latest , latest ) - latest
body_size = abs(latest - latest )
if wick_size > (body_size * 2.0):
signals.append({
'type': 'BULLISH_CONFLUENCE',
'price': latest ,
'confidence': 'ULTRA-HIGH',
'reason': f"75% WR Logic: MTF Trend UP + 4H Sweep + RSI < 30 + Absorption Volume + Rejection Wick."
})
break
# Bearish Setup
if trend == 'DOWN':
for level in resistance_4h:
if latest > level and latest < level:
if latest > 70 and latest > avg_vol and latest < latest * 1.02:
wick_size = latest - max(latest , latest )
body_size = abs(latest - latest )
if wick_size > (body_size * 2.0):
signals.append({
'type': 'BEARISH_CONFLUENCE',
'price': latest ,
'confidence': 'ULTRA-HIGH',
'reason': f"75% WR Logic: MTF Trend DOWN + 4H Sweep + RSI > 70 + Absorption Volume + Rejection Wick."
})
break
return {
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'price': latest ,
'trend': trend,
'signals': signals
}
if __name__ == "__main__":
agent = HighAccuracyTradingAgent()
res = agent.analyze()
if res:
print(f"=== BTC High Accuracy Scanner ===")
print(f"Time: {res } | Price: ${res :.2f} | Trend: {res }")
if res :
for s in res :
print(f"!!! SIGNAL: {s } at {s } - {s }")
else:
print("Status: Scanning... No ultra-high probability setups currently.")
دستیار هوشمند ارز دیجیتال
ترمینال ترید بایتیکل نرمافزار جامع ترید و سرمایهگذاری در بازار ارز دیجیتال است و امکاناتی مانند دورههای آموزشی ترید و سرمایهگذاری، تریدینگ ویو بدون محدودیت، هوش مصنوعی استراتژی ساز ترید، کلیه دادههای بازارهای مالی شامل دادههای اقتصاد کلان، تحلیل احساسات بازار، تکنیکال و آنچین، اتصال و مدیریت حساب صرافیها و تحلیلهای لحظهای را برای کاربران فراهم میکند.

