[ہزاروں کی جنگ] کرنسی اور فیوچر کے لئے اہم اصلاحات (حصہ 2)

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。

# 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

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

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)

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

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,]

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

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);

<Figure size 1224x432 with 1 Axes>

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

<Figure size 1296x432 with 1 Axes>

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

      realised_profit    margin  unrealised_profit      total  leverage  \
USDT        11912.530  2178.288           -627.858  21289.819      2.08   
ETC         -2991.311   248.555            548.893        NaN       NaN   
XLM           385.387   232.370           -722.603        NaN       NaN   
XMR           732.287   246.264           -624.718        NaN       NaN   
DASH        -1542.852    11.903             -1.935        NaN       NaN   
QTUM         3809.323    16.263              4.744        NaN       NaN   
ATOM         1439.716   115.502            161.850        NaN       NaN   
LTC           258.922    73.971            -80.578        NaN       NaN   
BAT          1646.675    38.881            -32.371        NaN       NaN   
NEO          1059.462    43.447             31.058        NaN       NaN   
IOST         3420.670    97.450             30.992        NaN       NaN   
XRP           849.192   213.615           -527.693        NaN       NaN   
VET          2071.060   117.874             42.525        NaN       NaN   
EOS           545.744    20.515             -9.696        NaN       NaN   
IOTA         -835.407   133.579            268.423        NaN       NaN   
BNB           829.466    66.933            -71.345        NaN       NaN   
TRX           956.784   139.170           -246.592        NaN       NaN   
ADA           643.538    30.378            -22.442        NaN       NaN   
ONT          1254.977   212.245            435.103        NaN       NaN   
ZEC         -1149.949   119.372            192.563        NaN       NaN   

          amount  hold_price     value   price  
USDT         NaN         NaN       NaN     NaN  
ETC      965.035       5.151  5520.000   5.720  
XLM  -106695.808       0.044  5370.000   0.050  
XMR      -95.657      51.489  5550.000  58.020  
DASH      -3.215      74.058   240.000  74.660  
QTUM     232.394       1.400   330.000   1.420  
ATOM    1021.867       2.261  2471.897   2.419  
LTC      -33.419      44.269  1560.000  46.680  
BAT    -4759.107       0.163   810.000   0.170  
NEO      112.994       7.690   900.000   7.965  
IOST  568312.285       0.003  1980.000   0.003  
XRP   -23964.054       0.178  4800.000   0.200  
VET   610687.023       0.004  2400.000   0.004  
EOS     -153.229       2.678   420.000   2.741  
IOTA   17294.118       0.154  2940.000   0.170  
BNB      -91.923      14.563  1410.000  15.339  
TRX  -221006.565       0.013  3030.000   0.014  
ADA   -17170.891       0.035   630.000   0.037  
ONT    10746.269       0.395  4680.000   0.436  
ZEC       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。

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);

<Figure size 1224x432 with 1 Axes>

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

      realised_profit    margin  unrealised_profit     total  leverage  \
USDT         9902.906  4027.894          -2697.901  17185.81     4.798   
ETC         -4406.699   325.615            747.701       NaN       NaN   
XLM          -702.556   161.279           -404.421       NaN       NaN   
XMR          -339.223   173.157           -346.855       NaN       NaN   
QTUM         2954.343    97.759            114.817       NaN       NaN   
ATOM          158.038   192.175            367.885       NaN       NaN   
BAT           607.179    45.790             44.208       NaN       NaN   
VET           714.430   195.911            221.770       NaN       NaN   
TRX          -144.737    61.477            -60.454       NaN       NaN   
ADA          -472.192    53.348             43.039       NaN       NaN   
ONT          -275.143   288.362            652.759       NaN       NaN   
ETH          3903.529   355.890          -1462.194       NaN       NaN   
LTC          -757.241     8.823              3.535       NaN       NaN   
BCH         -1850.977    14.829             33.416       NaN       NaN   
NEO          -171.220   123.249            175.023       NaN       NaN   
IOST         2473.843   178.499            150.019       NaN       NaN   
XRP          -132.605   137.268           -284.635       NaN       NaN   
EOS          -597.837    63.822             43.564       NaN       NaN   
IOTA        -2207.545   209.713            515.735       NaN       NaN   
BNB          -118.799    16.012              9.757       NaN       NaN   
LINK         9126.578   560.173          -2056.539       NaN       NaN   
DASH        -2591.464    72.126             57.486       NaN       NaN   
ZEC         -2505.799   196.170            396.599       NaN       NaN   
XTZ          9296.893   496.446          -1681.088       NaN       NaN   

           amount  hold_price      value    price  
USDT          NaN         NaN        NaN      NaN  
ETC      1269.231       5.131   7260.000    5.720  
XLM    -72123.982       0.045   3630.000    0.050  
XMR       -65.667      52.738   3810.000   58.020  
QTUM     1457.746       1.341   2070.000    1.420  
ATOM     1740.959       2.208   4211.379    2.419  
BAT      5640.423       0.162    960.000    0.170  
VET   1053435.115       0.004   4140.000    0.004  
TRX    -94091.904       0.013   1290.000    0.014  
ADA     30253.475       0.035   1110.000    0.037  
ONT     14741.676       0.391   6420.000    0.436  
ETH       -49.650     143.360   8580.000  172.810  
LTC         3.856      45.763    180.000   46.680  
BCH         1.196     247.881    330.000  275.810  
NEO       331.450       7.437   2640.000    7.965  
IOST  1067738.232       0.003   3720.000    0.003  
XRP    -15127.309       0.181   3030.000    0.200  
EOS       481.576       2.651   1320.000    2.741  
IOTA    27705.882       0.151   4710.000    0.170  
BNB        21.514      14.885    330.000   15.339  
LINK    -4672.304       2.398  13260.000    2.838  
DASH       20.091      71.799   1500.000   74.660  
ZEC       113.654      34.520   4320.000   38.010  
XTZ     -5657.895       1.755  11610.000    2.052  

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

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。

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，

(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);

<Figure size 1224x432 with 1 Axes>

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

<Figure size 1296x432 with 1 Axes>

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

      realised_profit  margin  unrealised_profit      total  leverage  \
USDT         2341.718  82.776             -4.166  12332.258     0.108   
ETC           176.759   2.964             -0.720        NaN       NaN   
XLM           -66.953   1.500              0.000        NaN       NaN   
XMR             1.846   3.000              0.000        NaN       NaN   
QTUM          183.606   4.479              0.429        NaN       NaN   
ATOM           45.535   1.472              1.080        NaN       NaN   
BAT            70.323  -0.000              0.000        NaN       NaN   
VET           334.754   5.881              2.372        NaN       NaN   
TRX           139.098   4.443              1.136        NaN       NaN   
ADA            49.083   4.479              0.423        NaN       NaN   
ONT           194.932   1.500             -0.000        NaN       NaN   
ETH           -66.459   2.964             -0.724        NaN       NaN   
LTC            71.310   1.500              0.000        NaN       NaN   
BCH             8.745  18.806            -13.890        NaN       NaN   
NEO           235.541   1.460              0.804        NaN       NaN   
IOST          340.620   5.942              1.160        NaN       NaN   
XRP            88.418   3.000              0.000        NaN       NaN   
EOS           205.702   1.469             -0.624        NaN       NaN   
IOTA          156.997   0.000              0.000        NaN       NaN   
BNB           101.521   4.500             -0.000        NaN       NaN   
LINK          188.646   6.000              0.000        NaN       NaN   
DASH          304.442   1.500             -0.000        NaN       NaN   
ZEC           230.375   0.000              0.000        NaN       NaN   
XTZ           481.436   5.918             -1.639        NaN       NaN   

           fee     amount  hold_price    value    price  
USDT  1134.452        NaN         NaN      NaN      NaN  
ETC     54.890    -10.490       5.651   60.000    5.720  
XLM     40.644    596.066       0.050   30.000    0.050  
XMR     53.261      1.034      58.020   60.000   58.020  
QTUM    35.068     63.380       1.413   90.000    1.420  
ATOM    60.876     12.616       2.333   30.517    2.419  
BAT     50.697      0.000       0.170    0.000    0.170  
VET     53.755  30534.351       0.004  120.000    0.004  
TRX     43.535   6564.551       0.014   90.000    0.014  
ADA     38.727   2452.984       0.037   90.000    0.037  
ONT     41.289    -68.886       0.436   30.000    0.436  
ETH     37.341     -0.347     170.726   60.000  172.810  
LTC     39.389      0.643      46.680   30.000   46.680  
BCH     47.110     -1.414     265.987  390.000  275.810  
NEO     36.081      3.766       7.751   30.000    7.965  
IOST    43.056  34443.169       0.003  120.000    0.003  
XRP     39.832    299.551       0.200   60.000    0.200  
EOS     42.125    -10.945       2.684   30.000    2.741  
IOTA    50.647      0.000       0.166    0.000    0.170  
BNB     47.154     -5.867      15.339   90.000   15.339  
LINK    79.517     42.283       2.838  120.000    2.838  
DASH    53.829     -0.402      74.660   30.000   74.660  
ZEC     52.561      0.000      37.220    0.000   38.010  
XTZ     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。

#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

#(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);

<Figure size 1224x432 with 1 Axes>

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

<Figure size 1296x432 with 1 Axes>

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

      realised_profit   margin  unrealised_profit      total  leverage  \
USDT         7635.210  188.662              0.455  17624.159     0.228   
ETC           485.916    4.500             -0.000        NaN       NaN   
XLM          -359.482    1.474             -0.513        NaN       NaN   
XMR           457.349   22.260              4.794        NaN       NaN   
DASH          244.654    2.887              2.265        NaN       NaN   
QTUM         1277.073    7.500              0.000        NaN       NaN   
ATOM          -30.950    1.483             -0.336        NaN       NaN   
LTC           -31.417    8.817             -3.651        NaN       NaN   
BAT           477.158   27.264            -24.723        NaN       NaN   
NEO           -60.144    2.938              1.243        NaN       NaN   
IOST         1529.663   29.673              6.540        NaN       NaN   
XRP           317.382    7.356              2.888        NaN       NaN   
VET           931.412   18.113              2.251        NaN       NaN   
EOS           409.221    1.500             -0.000        NaN       NaN   
IOTA          953.180    2.898              2.034        NaN       NaN   
BNB           829.692   12.000             -0.000        NaN       NaN   
TRX           258.911   19.124              7.516        NaN       NaN   
ADA           -37.350    8.642             -7.167        NaN       NaN   
ONT          1000.420    1.500             -0.000        NaN       NaN   
ZEC           628.931    8.733             -5.339        NaN       NaN   

          amount  hold_price  value   price  
USDT         NaN         NaN    NaN     NaN  
ETC      -15.734       5.720   90.0   5.720  
XLM     -596.066       0.049   30.0   0.050  
XMR        7.756      57.402  450.0  58.020  
DASH       0.804      71.841   60.0  74.660  
QTUM     105.634       1.420  150.0   1.420  
ATOM     -12.402       2.392   30.0   2.419  
LTC       -3.856      45.733  180.0  46.680  
BAT    -3349.001       0.163  570.0   0.170  
NEO        7.533       7.800   60.0   7.965  
IOST  172215.844       0.003  600.0   0.003  
XRP      748.877       0.196  150.0   0.200  
VET   -91603.053       0.004  360.0   0.004  
EOS      -10.945       2.741   30.0   2.741  
IOTA     352.941       0.164   60.0   0.170  
BNB      -15.646      15.339  240.0  15.339  
TRX    28446.389       0.013  390.0   0.014  
ADA    -4905.969       0.035  180.0   0.037  
ONT      -68.886       0.436   30.0   0.436  
ZEC       -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：

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']))

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