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add maa and fix bug.

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This commit is contained in:
aazhou 2023-06-19 21:52:46 +08:00
parent dbc0f96e7c
commit 69e26e5dd2
7 changed files with 78 additions and 128 deletions
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4
bn.py
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@ -3,8 +3,8 @@ from binance.spot import Spot
client = Spot() client = Spot()
# Get klines # Get klines
def klines(symbol, interval): def klines(symbol, interval, limit=1000):
lines = client.klines(symbol,interval) lines = client.klines(symbol,interval, limit=limit)
return lines return lines
# Get Symbols # Get Symbols

22
main.py
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@ -2,29 +2,21 @@ import schedule
import bn import bn
import settings import settings
import time import time
import strategy.crossover as crossover import strategy.ma_arrangement as maa
import strategy.large_trans as lt import strategy.large_trans as lt
# 获取交易所交易对 # 获取交易所交易对
# symbols = bn.symbols() symbols = bn.symbols()
# for s in symbols: for s in symbols:
# 5m #15m
# schedule.every(settings.whaleAlert_minutes).minutes.do(lt.strategy_run) schedule.every(15).minutes.do(maa.strategy_run, symbol=s, interval='15m')
# 5m
# schedule.every(5).minutes.do(crossover.strategy_run, symbol=s, interval='5m')
# 30m
# schedule.every(30).minutes.do(crossover.strategy_run, symbol=s, interval='30m')
#1h #1h
# schedule.every(1).hours.do(crossover.strategy_run, symbol=s, interval='1h') schedule.every(1).hours.do(maa.strategy_run, symbol=s, interval='1h')
#4h #4h
# schedule.every(4).hours.do(crossover.strategy_run, symbol=s, interval='4h') schedule.every(4).hours.do(maa.strategy_run, symbol=s, interval='4h')
# 监控 # 监控
schedule.every(settings.whaleAlert_minutes).minutes.do(lt.strategy_run) schedule.every(settings.whaleAlert_minutes).minutes.do(lt.strategy_run)

59
strategy/crossover.py
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@ -1,59 +0,0 @@
import talib
import numpy as np
import bn
import tg
import settings
# 检查是否出现多头排列信号
def check_bullish_crossover(data):
# 提取收盘价
close_prices = np.array([float(entry[4]) for entry in data])
# 计算移动平均线
ema7 = talib.EMA(close_prices, timeperiod=7)
ema30 = talib.EMA(close_prices, timeperiod=30)
ema100 = talib.EMA(close_prices, timeperiod=100)
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] and ema7[-2] <= ema30[-2] <= ema100[-2]:
return True
else:
return False
# 检查是否出现空头排列信号
def check_bearish_crossover(data):
# 提取收盘价
close_prices = np.array([float(entry[4]) for entry in data])
# 计算移动平均线
ema7 = talib.EMA(close_prices, timeperiod=7)
ema30 = talib.EMA(close_prices, timeperiod=30)
ema100 = talib.EMA(close_prices, timeperiod=100)
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] >= 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
def strategy_run(symbol, interval):
# 获取kline数据
data = bn.klines(symbol, interval)
if check_bullish_crossover(data):
print('多头排列信号出现！')
text = f'${symbol} - {interval}\r\n\r\n出现【多头排列】信号'
tg.send_message(settings.chat_id, text)
if check_bearish_crossover(data):
print("空头排列信号出现！")
text = f'${symbol} - {interval}\r\n\r\n出现【空头排列】信号'
tg.send_message(settings.chat_id, text)

12
strategy/large_trans.py
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@ -9,6 +9,7 @@ import logging
def strategy_run(): def strategy_run():
try:
start_time = datetime.now() - timedelta(minutes=settings.whaleAlert_minutes) start_time = datetime.now() - timedelta(minutes=settings.whaleAlert_minutes)
start_ts = int(start_time.timestamp()) start_ts = int(start_time.timestamp())
@ -19,21 +20,20 @@ def strategy_run():
print(resp) print(resp)
if resp['result'] == "success": if resp['result'] == "success":
cursor = resp['cursor']
try:
if 'transactions' in resp: if 'transactions' in resp:
data = resp['transactions'] data = resp['transactions']
for ts in data: for ts in data:
if ts['to']['owner_type'] == 'exchange': if ts['to']['owner_type'] == 'exchange':
from_text = 'unknown wallet' from_text = 'unknown wallet'
content = f"🚨 {ts['amount']} #{ts['symbol']} ({ts['amount_usd']} USD) 从 {from_text} 转入 #{ts['to']['owner']}" content = f"🚨大额转入提醒🚨\r\n\r\n {ts['amount']} #{ts['symbol']} ({ts['amount_usd']} USD) 从 {from_text} 转入 #{ts['to']['owner']}"
print(content) print(content)
tg.send_message(settings.chat_id, content) tg.send_message(settings.chat_id, content)
except Exception as e:
logging.error(traceback.format_exc())
tg.send_message(settings.chat_id, traceback.format_exc())
else: else:
print(resp['message']) print(resp['message'])
tg.send_message(settings.chat_id, resp['message'])
except:
logging.error(traceback.format_exc())
tg.send_message(settings.chat_id, traceback.format_exc()) tg.send_message(settings.chat_id, traceback.format_exc())

39
strategy/ma_arrangement.py Normal file
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@ -0,0 +1,39 @@
import talib
import numpy as np
import bn
import tg
import datetime
import settings
def check_ma_arrange(data):
# 提取收盘价
close_prices = np.array([float(entry[4]) for entry in data])
# 计算移动平均线
ema7 = talib.EMA(close_prices, timeperiod=7)
ema30 = talib.EMA(close_prices, timeperiod=30)
ema100 = talib.EMA(close_prices, timeperiod=100)
ema200 = talib.EMA(close_prices, timeperiod=200)
bullish = ema7[-1] > ema30[-1] > ema100[-1] > ema200[-1]
bearish = ema7[-1] < ema30[-1] < ema100[-1] < ema200[-1]
return bullish, bearish
def strategy_run(symbol, interval):
# 获取kline数据
data = bn.klines(symbol, interval)
bullish, bearish = check_ma_arrange(data)
text=""
if bullish:
text = f'📶信号预警📶\r\n\r\n品种：【${symbol}】\r\n周期：{interval}\r\n信号：【多头】排列\r\n\r\n{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}'
if bearish:
text = f'📶信号预警📶\r\n\r\n品种：【${symbol}】\r\n周期：{interval}\r\n信号：【空头】排列\r\n\r\n{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}'
if text != "":
print(text)
tg.send_message(settings.chat_id, text)

7
strategy_test.py
View File

@ -1,3 +1,8 @@
import strategy.large_trans as lt import strategy.large_trans as lt
import bn
import strategy.ma_arrangement as maa
lt.strategy_run() # symbols = bn.symbols()
# for s in symbols:
maa.strategy_run('LINKUSDT', '15m')

37
test.py
View File

@ -4,39 +4,12 @@ import mplfinance as mpf
import datetime as dt import datetime as dt
import strategy.large_trans as lt import strategy.large_trans as lt
# 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') klines = bn.klines('BTCUSDT', '1h', limit=10)
# print(klines[0])
# return df # print(klines[0][0])
dt = dt.datetime.fromtimestamp((klines[-1][0]/1000))
print(dt)
# 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')
# lt.strategy_run()
from datetime import datetime, timedelta
import time,settings
last_min = datetime.now() - timedelta(minutes=settings.whaleAlert_minutes)
print(last_min)
print(time.mktime(last_min.timetuple()))
print(last_min.timestamp())