1

datasource/crypto.py

@@ -0,0 +1,91 @@
+import requests
+from binance.spot import Spot
+import pandas as pd
+
+
+api_key = "HCpeel8g6fsTK2630b7BvGBcS09Z3qfXkLVcAY2JkpaiMm1J6DWRvoQZBQlElDJg"
+api_secret= "TySs6onlHOTrGzV8fMdDxLKTWWYnQ4rCHVAmjrcHby17acKflmo7xVTWVsbqtxe7"
+
+client = Spot(api_key, api_secret)
+
+
+
+# 获取市值前10的币种
+def _get_top_coins_by_market_cap(top):
+    
+    coingecko_url = "https://api.coingecko.com/api/v3/coins/markets"
+    params = {
+        'vs_currency': 'usd',
+        'order': 'market_cap_desc',
+        'per_page': top,
+        'page': 1
+    }
+    response = requests.get(coingecko_url, params=params)
+    coins = response.json()
+    return coins
+
+# 获取Binance上的USDT交易对信息
+def _get_binance_usdt_pairs():
+    url = "https://api.binance.com/api/v3/exchangeInfo"
+    response = requests.get(url)
+    data = response.json()
+
+    usdt_pairs = [symbol['symbol'] for symbol in data['symbols'] if (symbol['quoteAsset'] == 'USDT' and symbol['status'] == 'TRADING')]
+    return usdt_pairs
+
+def get_top_binance_usdt_pairs(top):
+    # 获取市值前10的币种
+    top_coins = _get_top_coins_by_market_cap(top)
+    
+    # 获取Binance上的USDT交易对
+    usdt_pairs = _get_binance_usdt_pairs()
+
+
+    # 筛选出前10币种中与USDT有交易对的币种
+    top_pairs = []
+    for coin in top_coins:
+        coin_symbol = coin['symbol'].upper()
+        pair = f"{coin_symbol}USDT"
+        if pair in usdt_pairs:
+            top_pairs.append(pair)
+    
+    return top_pairs
+
+## 获取所有现货交易对
+def get_symbols():
+    data = client.exchange_info()["symbols"]
+
+    # 创建DataFrame
+    columns = ['symbol', 'status', 'baseAsset', 'quoteAsset']
+    df = pd.DataFrame(data, columns=columns)
+
+    # 过滤出在架交易对
+    df_in_trade = df[df['status'] == 'TRADING']
+    df_in_USDT = df_in_trade[df_in_trade['quoteAsset'] == 'USDT']
+
+    return df_in_USDT['symbol']
+
+## 根据交易对和周期获取数据集
+def get_klines(symbol,interval):
+    # 获取 k 线数据
+    data = client.klines(symbol, interval,limit=500)
+
+    # 将数据转换为DataFrame
+    columns = ['timestamp', '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 = pd.DataFrame(data, columns=columns)
+
+    # 转化成 float
+    df['open'] = df['open'].astype('float64')
+    df['high'] = df['high'].astype('float64')
+    df['low'] = df['low'].astype('float64')
+    df['close'] = df['close'].astype('float64')
+    df['volume'] = df['volume'].astype('float64')
+
+    # 将时间戳转换为日期时间格式
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    df['close_time'] = pd.to_datetime(df['close_time'], unit='ms')
+
+    return df

main.py

@@ -8,8 +8,8 @@ import utils
 # 监控
 schedule.every(setting.whaleAlert_minutes).minutes.do(lt.run)
 
-schedule.every().hour.at(":00").do(vegas.run, interval = '1h')
+schedule.every().hour.at(":01").do(vegas.run_crypto, interval = '1h')
-schedule.every().hour.at(":00").do(vegas.run, interval = '4h')
+schedule.every().hour.at(":01").do(vegas.run_crypto, interval = '4h')
 
 print(f'Running... V1.0')
 print(f'Pairs:')

monitors/vegas.py

@@ -2,54 +2,10 @@ from binance.spot import Spot
 import talib
 import numpy as np
 import pandas as pd
+import datasource.crypto
 import time,setting
 import telegram_sender
-import utils
+from datasource import crypto
-
-
-api_key = "HCpeel8g6fsTK2630b7BvGBcS09Z3qfXkLVcAY2JkpaiMm1J6DWRvoQZBQlElDJg"
-api_secret= "TySs6onlHOTrGzV8fMdDxLKTWWYnQ4rCHVAmjrcHby17acKflmo7xVTWVsbqtxe7"
-
-client = Spot(api_key, api_secret)
-
-## 获取所有现货交易对
-def get_symbols():
-    data = client.exchange_info()["symbols"]
-
-    # 创建DataFrame
-    columns = ['symbol', 'status', 'baseAsset', 'quoteAsset']
-    df = pd.DataFrame(data, columns=columns)
-
-    # 过滤出在架交易对
-    df_in_trade = df[df['status'] == 'TRADING']
-    df_in_USDT = df_in_trade[df_in_trade['quoteAsset'] == 'USDT']
-
-    return df_in_USDT['symbol']
-
-## 根据交易对和周期获取数据集
-def get_dataFrame(symbol,interval):
-    # 获取 k 线数据
-    data = client.klines(symbol, interval,limit=500)
-
-    # 将数据转换为DataFrame
-    columns = ['timestamp', '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 = pd.DataFrame(data, columns=columns)
-
-    # 转化成 float
-    df['open'] = df['open'].astype('float64')
-    df['high'] = df['high'].astype('float64')
-    df['low'] = df['low'].astype('float64')
-    df['close'] = df['close'].astype('float64')
-    df['volume'] = df['volume'].astype('float64')
-
-    # 将时间戳转换为日期时间格式
-    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
-    df['close_time'] = pd.to_datetime(df['close_time'], unit='ms')
-
-    return df
 
 # crossover 函数：检测上穿信号
 def crossover(series1, series2):
@@ -60,7 +16,7 @@ def crossunder(series1, series2):
     return (series1 < series2) & (series1.shift(1) >= series2.shift(1))
 
 ## 检查信号
-def check_signal(symbol, interval,df):
+def stratergy_run(symbol, interval,df):
     ## 计算 ema
     df['ema13'] = talib.EMA(df['close'], timeperiod=13)
     df['ema144'] = talib.EMA(df['close'], timeperiod=144)
@@ -97,13 +53,13 @@ def check_signal(symbol, interval,df):
     if direction != "":
         telegram_sender.send_message(setting.chat_id, message)
     
-def run(interval):
+
+def run_crypto(interval):
     print('Vegas策略运行....')
-    symbols = utils.get_top_binance_usdt_pairs(20)
+
+    symbols = crypto.get_top_binance_usdt_pairs(10)
     for s in symbols:
-        df = get_dataFrame(s, interval)
+        df = crypto.get_klines(s, interval)
-        check_signal(s,interval, df)
+        stratergy_run(s,interval, df)
         
         time.sleep(1)
-
-

test.py

@@ -1,21 +1,26 @@
-import requests
+import yfinance as yf
+import pandas as pd
+import monitors
+import monitors.vegas
+
+# monitors.vegas.run('1d')
+
+
+
+import yfinance as yf
 import pandas as pd
 
-api_url = "https://api.exness.com/v1/ohlc"
+# 定义股票代码（例如苹果公司的股票代码为AAPL）
-params = {
+ticker = 'AAPL'
-    'symbol': 'XAUUSDm',
-    'interval': '1d',
-    'start': '2020-01-01',
-    'end': '2023-01-01'
-}
 
-# 发起HTTP请求
+# 下载日线数据
-response = requests.get(api_url, params=params)
+data = yf.download(ticker, interval='1d', period='2y')  # 过去一年的数据
-data = response.json()
 
-# 转换为DataFrame
+# 打印数据
-df = pd.DataFrame(data['ohlc'], columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+print(data)
-df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
 
-# 打印前五行数据
+# 如果需要将时间格式转换为可读格式
-print(df.head())
+data.index = pd.to_datetime(data.index)
+
+# 打印前几行数据
+print(data.head())