FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）
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2023-08-02 16:45:17
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币安做空超涨做多超跌策略的优化.ipynb下载 

币安做空超涨做多超跌策略的优化

原有研究报告地址：https://www.fmz.com/digest-topic/5294 可以先看一遍，这篇文章不会有重复的内容。重点介绍了第二个策略的优化过程。经过优化策略改进明显，建议根据这篇文章进行策略的升级。回测引擎加入了手续费的统计。

In [1]:

# 需要导入的库
import pandas as pd
import requests
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
%matplotlib inline

In [2]:

symbols = [\'ETH\', \'BCH\', \'XRP\', \'EOS\', \'LTC\', \'TRX\', \'ETC\', \'LINK\', \'XLM\', \'ADA\', \'XMR\', \'DASH\', \'ZEC\', \'XTZ\', \'BNB\', \'ATOM\', \'ONT\', \'IOTA\', \'BAT\', \'VET\', \'NEO\', \'QTUM\', \'IOST\']

In [3]:

price_usdt = pd.read_csv(\'https://www.fmz.com/upload/asset/20227de6c1d10cb9dd1.csv \', index_col = 0)
price_usdt.index = pd.to_datetime(price_usdt.index)

In [170]:

price_usdt_norm = price_usdt/price_usdt.fillna(method=\'bfill\').iloc[0,]

In [171]:

price_usdt_btc = price_usdt.divide(price_usdt[\'BTC\'],axis=0)
price_usdt_btc_norm = price_usdt_btc/price_usdt_btc.fillna(method=\'bfill\').iloc[0,]

In [225]:

class Exchange:
    
    def __init__(self, trade_symbols, leverage=20, commission=0.00005,  initial_balance=10000, log=False):
        self.initial_balance = initial_balance #初始的资产
        self.commission = commission
        self.leverage = leverage
        self.trade_symbols = trade_symbols
        self.date = \'\'
        self.log = log
        self.df = pd.DataFrame(columns=[\'margin\',\'total\',\'leverage\',\'realised_profit\',\'unrealised_profit\'])
        self.account = {\'USDT\':{\'realised_profit\':0, \'margin\':0, \'unrealised_profit\':0, \'total\':initial_balance, \'leverage\':0, \'fee\':0}}
        for symbol in trade_symbols:
            self.account[symbol] = {\'amount\':0, \'hold_price\':0, \'value\':0, \'price\':0, \'realised_profit\':0, \'margin\':0, \'unrealised_profit\':0,\'fee\':0}
            
    def Trade(self, symbol, direction, price, amount, msg=\'\'):
        if self.date and self.log:
            print(\'%-20s%-5s%-5s%-10.8s%-8.6s %s\'%(str(self.date), symbol, \'buy\' if direction == 1 else \'sell\', price, amount, msg))
            
        cover_amount = 0 if direction*self.account[symbol][\'amount\'] >=0 else min(abs(self.account[symbol][\'amount\']), amount)
        open_amount = amount - cover_amount
        
        self.account[\'USDT\'][\'realised_profit\'] -= price*amount*self.commission #扣除手续费
        self.account[\'USDT\'][\'fee\'] += price*amount*self.commission
        self.account[symbol][\'fee\'] += price*amount*self.commission
        
        if cover_amount > 0: #先平仓
            self.account[\'USDT\'][\'realised_profit\'] += -direction*(price - self.account[symbol][\'hold_price\'])*cover_amount  #利润
            self.account[\'USDT\'][\'margin\'] -= cover_amount*self.account[symbol][\'hold_price\']/self.leverage #释放保证金
            
            self.account[symbol][\'realised_profit\'] += -direction*(price - self.account[symbol][\'hold_price\'])*cover_amount
            self.account[symbol][\'amount\'] -= -direction*cover_amount
            self.account[symbol][\'margin\'] -=  cover_amount*self.account[symbol][\'hold_price\']/self.leverage
            self.account[symbol][\'hold_price\'] = 0 if self.account[symbol][\'amount\'] == 0 else self.account[symbol][\'hold_price\']
            
        if open_amount > 0:
            total_cost = self.account[symbol][\'hold_price\']*direction*self.account[symbol][\'amount\'] + price*open_amount
            total_amount = direction*self.account[symbol][\'amount\']+open_amount
            
            self.account[\'USDT\'][\'margin\'] +=  open_amount*price/self.leverage            
            self.account[symbol][\'hold_price\'] = total_cost/total_amount
            self.account[symbol][\'amount\'] += direction*open_amount
            self.account[symbol][\'margin\'] +=  open_amount*price/self.leverage
            
        self.account[symbol][\'unrealised_profit\'] = (price - self.account[symbol][\'hold_price\'])*self.account[symbol][\'amount\']
        self.account[symbol][\'price\'] = price
        self.account[symbol][\'value\'] = abs(self.account[symbol][\'amount\'])*price
        
        return True
    
    def Buy(self, symbol, price, amount, msg=\'\'):
        self.Trade(symbol, 1, price, amount, msg)
        
    def Sell(self, symbol, price, amount, msg=\'\'):
        self.Trade(symbol, -1, price, amount, msg)
        
    def Update(self, date, close_price): #对资产进行更新
        self.date = date
        self.close = close_price
        self.account[\'USDT\'][\'unrealised_profit\'] = 0
        for symbol in self.trade_symbols:
            if np.isnan(close_price[symbol]):
                continue
            self.account[symbol][\'unrealised_profit\'] = (close_price[symbol] - self.account[symbol][\'hold_price\'])*self.account[symbol][\'amount\']
            self.account[symbol][\'price\'] = close_price[symbol]
            self.account[symbol][\'value\'] = abs(self.account[symbol][\'amount\'])*close_price[symbol]
            self.account[\'USDT\'][\'unrealised_profit\'] += self.account[symbol][\'unrealised_profit\']
            if self.date.hour in [0,8,16]:
                pass
                self.account[\'USDT\'][\'realised_profit\'] += -self.account[symbol][\'amount\']*close_price[symbol]*0.01/100
        
        self.account[\'USDT\'][\'total\'] = round(self.account[\'USDT\'][\'realised_profit\'] + self.initial_balance + self.account[\'USDT\'][\'unrealised_profit\'],6)
        self.account[\'USDT\'][\'leverage\'] = round(self.account[\'USDT\'][\'margin\']/self.account[\'USDT\'][\'total\'],4)*self.leverage
        self.df.loc[self.date] = [self.account[\'USDT\'][\'margin\'],self.account[\'USDT\'][\'total\'],self.account[\'USDT\'][\'leverage\'],self.account[\'USDT\'][\'realised_profit\'],self.account[\'USDT\'][\'unrealised_profit\']]

原来策略的表现，经过币种的筛选，表现的还不错，但是持仓依然很多,普遍在4倍左右

原理：

  • 1.更新行情和账户持仓，第一次运行会记录初始价格（新加入的币种按照加入时间计算）
  • 2.更新指数，指数是山寨币-比特币价格指数 = mean(sum((山寨币价格/比特币价格)/(山寨币初始价格/比特币初始价格)))
  • 3.根据偏离指数判断做多做空,根据偏离大小判断仓位
  • 4.下单，下单量由冰山委托决定，按照对手价成交（买入用卖一价）。
  • 5.再次循环

In [176]:

trade_symbols = list(set(symbols)-set([\'LINK\',\'XTZ\',\'BCH\', \'ETH\']))#剩余的币种
price_usdt_btc_norm_mean = price_usdt_btc_norm[trade_symbols].mean(axis=1)
e = Exchange(trade_symbols,initial_balance=10000,commission=0.0005,log=False)
trade_value = 300
for row in price_usdt.iloc[:].iterrows():
    e.Update(row[0], row[1])
    empty_value = 0
    for symbol in trade_symbols:
        price = row[1][symbol]
        if np.isnan(price):
            continue
        diff = price_usdt_btc_norm.loc[row[0],symbol] - price_usdt_btc_norm_mean[row[0]]
        aim_value = -trade_value*round(diff/0.01,1)
        now_value = e.account[symbol][\'value\']*np.sign(e.account[symbol][\'amount\'])
        empty_value += now_value
        if aim_value - now_value > 20:
            e.Buy(symbol, price, round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
        if aim_value - now_value < -20:
            e.Sell(symbol, price, -round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
stragey_2b = e
(stragey_2b.df[\'total\']/stragey_2b.initial_balance).plot(figsize=(17,6),grid = True);

Out[176]:

FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）

In [178]:

stragey_2b.df[\'leverage\'].plot(figsize=(18,6),grid = True); #杠杆

Out[178]:

FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）

In [177]:

pd.DataFrame(e.account).T.apply(lambda x:round(x,3)) #持仓

Out[177]:

realised_profit margin unrealised_profit total leverage amount hold_price value price
USDT 11912.530 2178.288 -627.858 21289.819 2.08 NaN NaN NaN NaN
ETC -2991.311 248.555 548.893 NaN NaN 965.035 5.151 5520.000 5.720
XLM 385.387 232.370 -722.603 NaN NaN -106695.808 0.044 5370.000 0.050
XMR 732.287 246.264 -624.718 NaN NaN -95.657 51.489 5550.000 58.020
DASH -1542.852 11.903 -1.935 NaN NaN -3.215 74.058 240.000 74.660
QTUM 3809.323 16.263 4.744 NaN NaN 232.394 1.400 330.000 1.420
ATOM 1439.716 115.502 161.850 NaN NaN 1021.867 2.261 2471.897 2.419
LTC 258.922 73.971 -80.578 NaN NaN -33.419 44.269 1560.000 46.680
BAT 1646.675 38.881 -32.371 NaN NaN -4759.107 0.163 810.000 0.170
NEO 1059.462 43.447 31.058 NaN NaN 112.994 7.690 900.000 7.965
IOST 3420.670 97.450 30.992 NaN NaN 568312.285 0.003 1980.000 0.003
XRP 849.192 213.615 -527.693 NaN NaN -23964.054 0.178 4800.000 0.200
VET 2071.060 117.874 42.525 NaN NaN 610687.023 0.004 2400.000 0.004
EOS 545.744 20.515 -9.696 NaN NaN -153.229 2.678 420.000 2.741
IOTA -835.407 133.579 268.423 NaN NaN 17294.118 0.154 2940.000 0.170
BNB 829.466 66.933 -71.345 NaN NaN -91.923 14.563 1410.000 15.339
TRX 956.784 139.170 -246.592 NaN NaN -221006.565 0.013 3030.000 0.014
ADA 643.538 30.378 -22.442 NaN NaN -17170.891 0.035 630.000 0.037
ONT 1254.977 212.245 435.103 NaN NaN 10746.269 0.395 4680.000 0.436
ZEC -1149.949 119.372 192.563 NaN NaN 67.877 35.173 2580.000 38.010

为什么要改进

原有最大的问题是最新价格和策略启动的初始价格对比，随着时间的增长，会越来越偏离，我们将会在这些币种上累计非常多的仓位。过滤币种最大的问题是我们根据以往的经验而未来仍然可能出现特特立独行的币种。下面就是不过滤的表现，实际上在trade_value=300时，策略中期已经爆仓，即使不爆仓，LINK、XTZ也分别持有了10000USDT以上的仓位，实在过于大了。因此必须在回测中解决这个问题，通过全币种的考验。

In [98]:

trade_symbols = list(set(symbols))#剩余的币种
price_usdt_btc_norm_mean = price_usdt_btc_norm[trade_symbols].mean(axis=1)
e = Exchange(trade_symbols,initial_balance=10000,commission=0.0005,log=False)
trade_value = 300
for row in price_usdt.iloc[:].iterrows():
    e.Update(row[0], row[1])
    empty_value = 0
    for symbol in trade_symbols:
        price = row[1][symbol]
        if np.isnan(price):
            continue
        diff = price_usdt_btc_norm.loc[row[0],symbol] - price_usdt_btc_norm_mean[row[0]]
        aim_value = -trade_value*round(diff/0.01,1)
        now_value = e.account[symbol][\'value\']*np.sign(e.account[symbol][\'amount\'])
        empty_value += now_value
        if aim_value - now_value > 20:
            e.Buy(symbol, price, round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
        if aim_value - now_value < -20:
            e.Sell(symbol, price, -round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
stragey_2c = e
(stragey_2c.df[\'total\']/stragey_2c.initial_balance).plot(figsize=(17,6),grid = True);

Out[98]:

FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）

In [99]:

pd.DataFrame(stragey_2c.account).T.apply(lambda x:round(x,3)) #最后持仓

Out[99]:

realised_profit margin unrealised_profit total leverage amount hold_price value price
USDT 9902.906 4027.894 -2697.901 17185.81 4.798 NaN NaN NaN NaN
ETC -4406.699 325.615 747.701 NaN NaN 1269.231 5.131 7260.000 5.720
XLM -702.556 161.279 -404.421 NaN NaN -72123.982 0.045 3630.000 0.050
XMR -339.223 173.157 -346.855 NaN NaN -65.667 52.738 3810.000 58.020
QTUM 2954.343 97.759 114.817 NaN NaN 1457.746 1.341 2070.000 1.420
ATOM 158.038 192.175 367.885 NaN NaN 1740.959 2.208 4211.379 2.419
BAT 607.179 45.790 44.208 NaN NaN 5640.423 0.162 960.000 0.170
VET 714.430 195.911 221.770 NaN NaN 1053435.115 0.004 4140.000 0.004
TRX -144.737 61.477 -60.454 NaN NaN -94091.904 0.013 1290.000 0.014
ADA -472.192 53.348 43.039 NaN NaN 30253.475 0.035 1110.000 0.037
ONT -275.143 288.362 652.759 NaN NaN 14741.676 0.391 6420.000 0.436
ETH 3903.529 355.890 -1462.194 NaN NaN -49.650 143.360 8580.000 172.810
LTC -757.241 8.823 3.535 NaN NaN 3.856 45.763 180.000 46.680
BCH -1850.977 14.829 33.416 NaN NaN 1.196 247.881 330.000 275.810
NEO -171.220 123.249 175.023 NaN NaN 331.450 7.437 2640.000 7.965
IOST 2473.843 178.499 150.019 NaN NaN 1067738.232 0.003 3720.000 0.003
XRP -132.605 137.268 -284.635 NaN NaN -15127.309 0.181 3030.000 0.200
EOS -597.837 63.822 43.564 NaN NaN 481.576 2.651 1320.000 2.741
IOTA -2207.545 209.713 515.735 NaN NaN 27705.882 0.151 4710.000 0.170
BNB -118.799 16.012 9.757 NaN NaN 21.514 14.885 330.000 15.339
LINK 9126.578 560.173 -2056.539 NaN NaN -4672.304 2.398 13260.000 2.838
DASH -2591.464 72.126 57.486 NaN NaN 20.091 71.799 1500.000 74.660
ZEC -2505.799 196.170 396.599 NaN NaN 113.654 34.520 4320.000 38.010
XTZ 9296.893 496.446 -1681.088 NaN NaN -5657.895 1.755 11610.000 2.052

In [25]:

((price_usdt_btc_norm.iloc[-1:] - price_usdt_btc_norm_mean[-1]).T) #各个币种偏离初始的情况

Out[25]:

0 2020-04-08 03:00:00
BTC 0.165191
ETH 0.285501
BCH -0.011090
XRP 0.101466
EOS -0.044139
LTC -0.005890
TRX 0.042733
ETC -0.241950
LINK 0.442115
XLM 0.121035
ADA -0.037183
XMR 0.126688
DASH -0.050144
ZEC -0.143874
XTZ 0.386968
BNB -0.010699
ATOM -0.140183
ONT -0.213943
IOTA -0.156505
BAT -0.031535
VET -0.138276
NEO -0.088255
QTUM -0.068959
IOST -0.123881

既然问题的原因是和开始的价格进行比较，可能越偏越多。我们可以和过去一段时间的均线对比，回测全币种以下看看结果。

In [245]:

Alpha = 0.05
#price_usdt_btc_norm2 = price_usdt_btc/price_usdt_btc.rolling(20).mean() #普通均线
price_usdt_btc_norm2 = price_usdt_btc/price_usdt_btc.ewm(alpha=Alpha).mean() #这里和策略一致，用了EMA
trade_symbols = list(set(symbols))#全币种
price_usdt_btc_norm_mean = price_usdt_btc_norm2[trade_symbols].mean(axis=1)
e = Exchange(trade_symbols,initial_balance=10000,commission=0.0005,log=False)
trade_value = 300
for row in price_usdt.iloc[:].iterrows():
    e.Update(row[0], row[1])
    empty_value = 0
    for symbol in trade_symbols:
        price = row[1][symbol]
        if np.isnan(price):
            continue
        diff = price_usdt_btc_norm2.loc[row[0],symbol] - price_usdt_btc_norm_mean[row[0]]
        aim_value = -trade_value*round(diff/0.01,1)
        now_value = e.account[symbol][\'value\']*np.sign(e.account[symbol][\'amount\'])
        empty_value += now_value
        if aim_value - now_value > 20:
            e.Buy(symbol, price, round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
        if aim_value - now_value < -20:
            e.Sell(symbol, price, -round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
stragey_2d = e
#print(N,stragey_2d.df[\'total\'][-1],pd.DataFrame(stragey_2d.account).T.apply(lambda x:round(x,3))[\'value\'].sum())

策略表现完全达到了我们的预期，收益相差无几，在全币种原策略爆仓的局面也平滑过渡了，几乎没回撤，同样的开仓大小，杠杆几乎都在1倍以下，3.12暴跌的极端情况下也没超过4倍，这意味着我们可以加大trade_value，在同样的杠杆下，将收益再提高一倍。最终的持仓只有BCH超过1000USDT，十分良好。

为什么会降低了持仓，想象以下加入山寨币指数不变，一个币涨了100%，而且长期维持，原策略会长期持有300*100 = 30000USDT的空单，而新策略会最终将基准价格追踪到最新价，最后会不持仓，

In [246]:

(stragey_2d.df[\'total\']/stragey_2d.initial_balance).plot(figsize=(17,6),grid = True);
#(stragey_2c.df[\'total\']/stragey_2c.initial_balance).plot(figsize=(17,6),grid = True);

Out[246]:

FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）

In [247]:

stragey_2d.df[\'leverage\'].plot(figsize=(18,6),grid = True);
stragey_2b.df[\'leverage\'].plot(figsize=(18,6),grid = True); #筛选币种策略杠杆

Out[247]:

FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）

In [228]:

pd.DataFrame(stragey_2d.account).T.apply(lambda x:round(x,3))

Out[228]:

realised_profit margin unrealised_profit total leverage fee amount hold_price value price
USDT 2341.718 82.776 -4.166 12332.258 0.108 1134.452 NaN NaN NaN NaN
ETC 176.759 2.964 -0.720 NaN NaN 54.890 -10.490 5.651 60.000 5.720
XLM -66.953 1.500 0.000 NaN NaN 40.644 596.066 0.050 30.000 0.050
XMR 1.846 3.000 0.000 NaN NaN 53.261 1.034 58.020 60.000 58.020
QTUM 183.606 4.479 0.429 NaN NaN 35.068 63.380 1.413 90.000 1.420
ATOM 45.535 1.472 1.080 NaN NaN 60.876 12.616 2.333 30.517 2.419
BAT 70.323 -0.000 0.000 NaN NaN 50.697 0.000 0.170 0.000 0.170
VET 334.754 5.881 2.372 NaN NaN 53.755 30534.351 0.004 120.000 0.004
TRX 139.098 4.443 1.136 NaN NaN 43.535 6564.551 0.014 90.000 0.014
ADA 49.083 4.479 0.423 NaN NaN 38.727 2452.984 0.037 90.000 0.037
ONT 194.932 1.500 -0.000 NaN NaN 41.289 -68.886 0.436 30.000 0.436
ETH -66.459 2.964 -0.724 NaN NaN 37.341 -0.347 170.726 60.000 172.810
LTC 71.310 1.500 0.000 NaN NaN 39.389 0.643 46.680 30.000 46.680
BCH 8.745 18.806 -13.890 NaN NaN 47.110 -1.414 265.987 390.000 275.810
NEO 235.541 1.460 0.804 NaN NaN 36.081 3.766 7.751 30.000 7.965
IOST 340.620 5.942 1.160 NaN NaN 43.056 34443.169 0.003 120.000 0.003
XRP 88.418 3.000 0.000 NaN NaN 39.832 299.551 0.200 60.000 0.200
EOS 205.702 1.469 -0.624 NaN NaN 42.125 -10.945 2.684 30.000 2.741
IOTA 156.997 0.000 0.000 NaN NaN 50.647 0.000 0.166 0.000 0.170
BNB 101.521 4.500 -0.000 NaN NaN 47.154 -5.867 15.339 90.000 15.339
LINK 188.646 6.000 0.000 NaN NaN 79.517 42.283 2.838 120.000 2.838
DASH 304.442 1.500 -0.000 NaN NaN 53.829 -0.402 74.660 30.000 74.660
ZEC 230.375 0.000 0.000 NaN NaN 52.561 0.000 37.220 0.000 38.010
XTZ 481.436 5.918 -1.639 NaN NaN 93.068 -58.480 2.024 120.000 2.052

有筛选币的情况会怎样，还是同样的参数，前期的收益表现更好，回撤更小，但收益稍低。因此还是推荐筛选的。

In [199]:

#price_usdt_btc_norm2 = price_usdt_btc/price_usdt_btc.rolling(50).mean()
price_usdt_btc_norm2 = price_usdt_btc/price_usdt_btc.ewm(alpha=0.05).mean()
trade_symbols = list(set(symbols)-set([\'LINK\',\'XTZ\',\'BCH\', \'ETH\']))#剩余的币种
price_usdt_btc_norm_mean = price_usdt_btc_norm2[trade_symbols].mean(axis=1)
e = Exchange(trade_symbols,initial_balance=10000,commission=0.0005,log=False)
trade_value = 300
for row in price_usdt.iloc[:].iterrows():
    e.Update(row[0], row[1])
    empty_value = 0
    for symbol in trade_symbols:
        price = row[1][symbol]
        if np.isnan(price):
            continue
        diff = price_usdt_btc_norm2.loc[row[0],symbol] - price_usdt_btc_norm_mean[row[0]]
        aim_value = -trade_value*round(diff/0.01,1)
        now_value = e.account[symbol][\'value\']*np.sign(e.account[symbol][\'amount\'])
        empty_value += now_value
        if aim_value - now_value > 20:
            e.Buy(symbol, price, round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
        if aim_value - now_value < -20:
            e.Sell(symbol, price, -round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
stragey_2e = e

In [200]:

#(stragey_2d.df[\'total\']/stragey_2d.initial_balance).plot(figsize=(17,6),grid = True);
(stragey_2e.df[\'total\']/stragey_2e.initial_balance).plot(figsize=(17,6),grid = True);

Out[200]:

FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）

In [192]:

stragey_2e.df[\'leverage\'].plot(figsize=(18,6),grid = True);

Out[192]:

FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）FMZ量化交易[千团大战]币安期货做空超涨做多超跌策略重要优化（第2篇）

In [193]:

pd.DataFrame(stragey_2e.account).T.apply(lambda x:round(x,3))

Out[193]:

realised_profit margin unrealised_profit total leverage amount hold_price value price
USDT 7635.210 188.662 0.455 17624.159 0.228 NaN NaN NaN NaN
ETC 485.916 4.500 -0.000 NaN NaN -15.734 5.720 90.0 5.720
XLM -359.482 1.474 -0.513 NaN NaN -596.066 0.049 30.0 0.050
XMR 457.349 22.260 4.794 NaN NaN 7.756 57.402 450.0 58.020
DASH 244.654 2.887 2.265 NaN NaN 0.804 71.841 60.0 74.660
QTUM 1277.073 7.500 0.000 NaN NaN 105.634 1.420 150.0 1.420
ATOM -30.950 1.483 -0.336 NaN NaN -12.402 2.392 30.0 2.419
LTC -31.417 8.817 -3.651 NaN NaN -3.856 45.733 180.0 46.680
BAT 477.158 27.264 -24.723 NaN NaN -3349.001 0.163 570.0 0.170
NEO -60.144 2.938 1.243 NaN NaN 7.533 7.800 60.0 7.965
IOST 1529.663 29.673 6.540 NaN NaN 172215.844 0.003 600.0 0.003
XRP 317.382 7.356 2.888 NaN NaN 748.877 0.196 150.0 0.200
VET 931.412 18.113 2.251 NaN NaN -91603.053 0.004 360.0 0.004
EOS 409.221 1.500 -0.000 NaN NaN -10.945 2.741 30.0 2.741
IOTA 953.180 2.898 2.034 NaN NaN 352.941 0.164 60.0 0.170
BNB 829.692 12.000 -0.000 NaN NaN -15.646 15.339 240.0 15.339
TRX 258.911 19.124 7.516 NaN NaN 28446.389 0.013 390.0 0.014
ADA -37.350 8.642 -7.167 NaN NaN -4905.969 0.035 180.0 0.037
ONT 1000.420 1.500 -0.000 NaN NaN -68.886 0.436 30.0 0.436
ZEC 628.931 8.733 -5.339 NaN NaN -4.736 36.883 180.0 38.010

参数的优化

指数移动平局的Alpha参数，设置的越大，基准价格跟踪越敏感，交易的越少，最终持仓也会越低，降低了杠杆，但会降低收益，降低最大回撤，可以加大成交量，具体需要根据回测结果自己权衡。

由于回测是1hK线，只能一小时更新一次，实盘可以更快的更新，需要综合权衡具体设置多少。

这是优化的结果：

In [235]:

for Alpha in [i/100 for i in range(1,30)]:
    #price_usdt_btc_norm2 = price_usdt_btc/price_usdt_btc.rolling(20).mean() #普通均线
    price_usdt_btc_norm2 = price_usdt_btc/price_usdt_btc.ewm(alpha=Alpha).mean() #这里和策略一致，用了EMA
    trade_symbols = list(set(symbols))#全币种
    price_usdt_btc_norm_mean = price_usdt_btc_norm2[trade_symbols].mean(axis=1)
    e = Exchange(trade_symbols,initial_balance=10000,commission=0.0005,log=False)
    trade_value = 300
    for row in price_usdt.iloc[:].iterrows():
        e.Update(row[0], row[1])
        empty_value = 0
        for symbol in trade_symbols:
            price = row[1][symbol]
            if np.isnan(price):
                continue
            diff = price_usdt_btc_norm2.loc[row[0],symbol] - price_usdt_btc_norm_mean[row[0]]
            aim_value = -trade_value*round(diff/0.01,1)
            now_value = e.account[symbol][\'value\']*np.sign(e.account[symbol][\'amount\'])
            empty_value += now_value
            if aim_value - now_value > 20:
                e.Buy(symbol, price, round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
            if aim_value - now_value < -20:
                e.Sell(symbol, price, -round((aim_value - now_value)/price, 6),round(e.account[symbol][\'realised_profit\']+e.account[symbol][\'unrealised_profit\'],2))
    stragey_2d = e
    # 分别是最终净值，初始最大回测，最终持仓大小,手续费
    print(Alpha, round(stragey_2d.account[\'USDT\'][\'total\'],1), round(1-stragey_2d.df[\'total\'].min()/stragey_2d.initial_balance,2),round(pd.DataFrame(stragey_2d.account).T[\'value\'].sum(),1),round(stragey_2d.account[\'USDT\'][\'fee\']))

Out[235]:

0.01 21116.2 0.14 15480.0 2178.0
0.02 20555.6 0.07 12420.0 2184.0
0.03 20279.4 0.06 9990.0 2176.0
0.04 20021.5 0.04 8580.0 2168.0
0.05 19719.1 0.03 7740.0 2157.0
0.06 19616.6 0.03 7050.0 2145.0
0.07 19344.0 0.02 6450.0 2133.0
0.08 19174.0 0.02 6120.0 2117.0
0.09 18988.4 0.01 5670.0 2104.0
0.1 18734.8 0.01 5520.0 2090.0
0.11 18532.7 0.01 5310.0 2078.0
0.12 18354.2 0.01 5130.0 2061.0
0.13 18171.7 0.01 4830.0 2047.0
0.14 17960.4 0.01 4770.0 2032.0
0.15 17779.8 0.01 4531.3 2017.0
0.16 17570.1 0.01 4441.3 2003.0
0.17 17370.2 0.01 4410.0 1985.0
0.18 17203.7 0.0 4320.0 1971.0
0.19 17016.9 0.0 4290.0 1955.0
0.2 16810.6 0.0 4230.6 1937.0
0.21 16664.1 0.0 4051.3 1921.0
0.22 16488.2 0.0 3930.6 1902.0
0.23 16378.9 0.0 3900.6 1887.0
0.24 16190.8 0.0 3840.0 1873.0
0.25 15993.0 0.0 3781.3 1855.0
0.26 15828.5 0.0 3661.3 1835.0
0.27 15673.0 0.0 3571.3 1816.0
0.28 15559.5 0.0 3511.3 1800.0
0.29 15416.4 0.0 3481.3 1780.0

In [ ]:

FMZ量化交易平台邀请链接：https://www.fmz.com/

全球最大交易所币安，国区邀请链接：https://accounts.binance.com/zh-CN/register?ref=16003031  币安注册不了IP地址用香港，居住地选香港，认证照旧，邮箱推荐如gmail、outlook。支持币种多，交易安全！

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FMZ技术 FMZ操作方法 FMZ教程 FMZ量化交易教程 fmz量化平台教程 fmz量化教程 FMZ量化方法

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