问安装python bot，使其适合于Binance的期货交易

EN

import ccxt
import time
import config
import schedule
import numpy  as np
import pandas as pd; pd.set_option( 'display.max_rows', None )
import warnings;     warnings.filterwarnings( 'ignore' )

from   datetime import datetime
from   my_code  import tr, atr, supertrend, run_bot
from   fut_sig  import check_buy_sell_signals

#binancefutures
exchange = ccxt.binanceusdm( { 'apiKey': 'apiKey',
                               'secret': 'secret',
                                }
                             )
lev = exchange.set_leverage( 10, 'ETH/USDT' )
#----------------------------------#
global in_position                 # it does not save you here
#----------------------------------# on global scope

schedule.every( 1 ).minutes.do( run_bot )

while True:
    schedule.run_pending()
    time.sleep( 1 )

def check_buy_sell_signals(df):
    #------------------------------# it would get masked here
    global in_position             # if not DECLARED to be a global
    #------------------------------# so avoiding a new, local one from masking it
    in_position = False
    to_use = (exchange.fetch_balance().get('USDT').get('free'))
    price = ((exchange.fetchTicker('ETH/USDT').get('last')))-10
    bal = to_use / price
    print("Checking for buy or sell signals")
    print(df.tail(5))
    last_row_index = len(df.index)- 1
    previous_row_index = last_row_index - 1
    if not df['in_uptrend'][previous_row_index] and df['in_uptrend'][last_row_index]:
            print("changed to uptrend, buy")
            if not in_position:
                order = exchange.create_market_buy_order('ETH/USDT', bal)
                print(order)
                in_position = True
    if df['in_uptrend'][previous_row_index] and not df['in_uptrend'][last_row_index]:
            print("changed to downtrend, sell")
            if not in_position:
                order = exchange.create_market_sell_order('ETH/USDT', bal)
                print(order)
                in_position = True
    if df['in_uptrend'][previous_row_index] and not df['in_uptrend'][last_row_index]:
            print("trend changed")
            if in_position:
                close_position = binance.create_order(symbol=symbol, type="MARKET", side="sell", amount=pos['positionAmt'], params={"reduceOnly": True})
                print(order)
                in_position = False
    if not df['in_uptrend'][previous_row_index] and df['in_uptrend'][last_row_index]:
            print("trend changed")
            if in_position:
                order = exchange.create_market_buy_order('ETH/USDT', bal)
                close_position = binance.create_order(symbol=symbol, type="MARKET", side="buy", amount=pos['positionAmt'], params={"reduceOnly": True})
                print(order)
                in_position = False

def run_bot():
    print("Fetching new bars for", datetime.now())
    bars = exchange.fetch_ohlcv('ETH/USDT', timeframe='1h', limit=100)
    df = pd.DataFrame(bars[:-1], columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
    df['timestamp']=pd.to_datetime(df['timestamp'], unit='ms')
    supertrend_data = supertrend(df)
    check_buy_sell_signals(supertrend_data)

def tr(data):
    data['previous_close']=data['close'].shift(1)
    data['high-low']=abs(data['high'] - data['low'])
    data['high-pc']=abs(data['high'] - data['previous_close'])
    data['low-pc']=abs(data['low']-data['previous_close'])
    tr=data[['high-low', 'high-pc', 'low-pc']].max(axis=1)
    return tr

def atr(data, period=10):#supertrend variables
    data['tr']=tr(data)
    print("calculate average true range")
    atr=data['tr'].rolling(period).mean()
    return atr

def supertrend(df, period=10, atr_multiplier=1.4):
    hl2=(df['high']+df['low'])/2
    print("calculating supertrend")
    df['atr']=atr(df, period=period)
    df['upperband']= hl2 + (atr_multiplier*df['atr'])
    df['lowerband']= hl2 -(atr_multiplier*df['atr'])
    df['in_uptrend']=True
    for current in range(1, len(df.index)):
        previous=current-1
        if df['close'][current] > df['upperband'][previous]:
            df['in_uptrend'][current]=True
        elif df['close'][current] < df['lowerband'][previous]:
            df['in_uptrend'][current]=False
        else:
            df['in_uptrend'][current] = df['in_uptrend'][previous]
            if df['in_uptrend'][current] and df['lowerband'][current] < df['lowerband'][previous]:
                df['lowerband'][current] = df['lowerband'][previous]
            if not df['in_uptrend'][current] and df['upperband'][current] > df['upperband'][previous]:
                df['upperband'][current] = df['upperband'][previous]
    return(df)