update.

bn.py

@@ -2,7 +2,6 @@ from binance.spot import Spot
 
 client = Spot()
 
-
 # Get klines
 def klines(symbol, interval):
     lines = client.klines(symbol,interval)

strategy/crossover.py

@@ -16,7 +16,8 @@ def check_bullish_crossover(data):
     ema200 = talib.EMA(close_prices, timeperiod=200)
     
     # 判断是否出现多头排列信号
-    if ema7[-1] > ema30[-1] > ema100[-1] > ema200[-1] and ema7[-2] <= ema30[-2] <= ema100[-2] <= ema200[-2]:
+    # if ema7[-1] > ema30[-1] > ema100[-1] > ema200[-1] and ema7[-2] <= ema30[-2] <= ema100[-2] <= ema200[-2]:
+    if ema7[-1] > ema30[-1] > ema100[-1] and ema7[-2] <= ema30[-2] <= ema100[-2]:
         return True
     else:
         return False
@@ -33,7 +34,9 @@ def check_bearish_crossover(data):
     ema200 = talib.EMA(close_prices, timeperiod=200)
     
     # 判断是否出现空头排列信号
-    if ema7[-1] < ema30[-1] < ema100[-1] < ema200[-1] and ema7[-2] >= ema30[-2] >= ema100[-2] >= ema200[-2]:
+    if ema7[-1] < ema30[-1] < ema100[-1] < ema200[-1] >= ema30[-2] >= ema100[-2]:
+    # if ema7[-1] < ema30[-1] < ema100[-1] < ema200[-1] and ema7[-2] >= ema30[-2] >= ema100[-2] >= ema200[-2]:
+
         return True
     else:
         return False

strategy/support_resistance.py

@@ -0,0 +1,44 @@
+import talib
+import numpy as np
+import bn
+import tg
+import settings
+
+# 获取K线价格数据
+def get_prices(data):
+    # 提取最高价、最低价和收盘价
+    high_prices = [float(entry[2]) for entry in data]
+    low_prices = [float(entry[3]) for entry in data]
+    close_prices = [float(entry[4]) for entry in data]
+    return np.array(high_prices), np.array(low_prices), np.array(close_prices)
+
+
+# 计算支撑位和压力位
+def calculate_support_resistance(data):
+        # 获取价格数据
+    high_prices, low_prices, close_prices = get_prices(data)
+    
+    # 计算平均真实范围（ATR）
+    atr = talib.ATR(high_prices, low_prices, close_prices, timeperiod=14)
+    
+    # 计算支撑位和压力位
+    support = talib.SMA(close_prices - 1.618 * atr, timeperiod=20)
+    resistance = talib.SMA(close_prices + 1.618 * atr, timeperiod=20)
+
+    return support, resistance
+
+def strategy_run(symbol, interval):
+    # 获取kline数据
+    data = bn.klines(symbol, interval)
+
+    support_levels, resistance_levels = calculate_support_resistance(data)
+
+    # 打印支撑位和压力位
+    print("支撑位：", support_levels)
+    print("压力位：", resistance_levels)
+
+    # if check_bullish_crossover(data):
+    #     print('多头排列信号出现！')
+
+    #     text = f'${symbol} - {interval}\r\n\r\n出现【多头排列】信号'
+    #     tg.send_message(settings.chat_id, text)

strategy_test.py

@@ -0,0 +1,4 @@
+import strategy.support_resistance as sr
+
+
+sr.strategy_run('BTCUSDT', '1h')

test.py

@@ -0,0 +1,31 @@
+import bn
+import pandas as pd
+import mplfinance as mpf
+import datetime as dt
+
+def binanceDataFrame(klines):
+    df = pd.DataFrame(klines.reshape(-1,12),dtype=float, columns = ('Open Time',
+                                                                    'Open',
+                                                                    'High',
+                                                                    'Low',
+                                                                    'Close',
+                                                                    'Volume',
+                                                                    'Close time',
+                                                                    'Quote asset volume',
+                                                                    'Number of trades',
+                                                                    'Taker buy base asset volume',
+                                                                    'Taker buy quote asset volume',
+                                                                    'Ignore'))
+
+    df['Open Time'] = pd.to_datetime(df['Open Time'], unit='ms')
+
+
+    return df
+
+
+klines = bn.klines('BTCUSDT', '1h')
+
+df = pd.DataFrame(klines,dtype=float)
+df.columns = ['Open time', 'Open', 'High', 'Low', 'Close', 'Volume', 'ctime', 'Quote asset volume', 'Number of trades', 'Taker buy base asset volume', 'Taker buy quote asset volume', 'Can be ignored']
+df.index = [dt.datetime.fromtimestamp(x/1000.0) for x in df.ctime]
+mpf.plot(df, type='line')