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huobipro=ccxt.huobipro({
'apiKey':'',
'secret':'',
})
先使用ccxt获取交易所的实例，然后获取历史k线，得到的数据使用dataframe格式接受
huobipro.fetch_ohlcv(symbol=symbol,limit=limit_num,timeframe=timeframe)
然后利用pandas提供的函数计算MA，
df['median_short']=df['close'].rolling(n_short,min_periods=1).mean()
df['median_long']=df['close'].rolling(n_long,min_periods=1).mean()
然后再找出买入卖出信号，
#找出买入信号
condition1=df['median_short']>df['median_long']#短均线上穿长均线
condition2=df['median_short'].shift(1)<=df['median_long'].shift(1)
df.loc[condition1&condition2,'signal']=1#产生买入信号的k线标记为1
#找出卖出信号
condition1=df['median_short']<df['median_long']#短均线上穿长均线
condition2=df['median_short'].shift(1)>=df['median_long'].shift(1)
df.loc[condition1&condition2,'signal']=0#产生卖出信号的k线标记为0
有了交易信号，就可以获取信号，再判断进行下单（huobipro.create_limit_buy/sell_order（）了）
第五步：其实第四步就可以交易了，第五步是回测，一般来说先回测再根据回测结果选用策略，最后才进行实盘
回测分析的相关有很多种，在这方面我也不是很懂，目前我还是习惯用累计利润来进行分析，
#由signal计算出实际的每天持仓
df['pos']=df['signal'].shift()
df['pos'].fillna(method='ffill',inplace=True)
df['pos'].fillna(value=0,inplace=True)
到这里持仓信号就有了，就可以根据持仓和历史k线的价格计算累计利润了，
df['change']=df['close'].pct_change(1)#根据收盘价计算涨跌幅
df['by_at_open_change']=df['close']/df['open']-1#开盘买入到收盘的涨跌幅
df['sell_next_open_change']=df['open'].shift(-1)/df['close']-1#这根收盘到下根开盘的涨跌幅
df.at[len(df)-1,'sell_next_open_change']=0#补全空值df.at[4,'B']
condition1=df['pos']==1
condition2=df['pos']!=df['pos'].shift(1)
open_pos_condition=condition1&condition2
#选取平仓条件
condition1=df['pos']==0
condition2=df['pos']!=df['pos'].shift(1)
close_pos_condition=condition1&condition2
#对每次交易进行分组
df.loc[open_pos_condition,'start_time']=df['open_time']
df['start_time'].fillna(method='ffill',inplace=True)
df.loc[df['pos']==0,'start_time']=pd.NaT
init_cash=1000#初始资金
#计算仓位变动
#开仓时仓位
df.loc[open_pos_condition,'position']=init_cash*(1+df['by_at_open_change'])
group_num=len(df.groupby('start_time'))
if group_num>1:
temp=df.groupby('start_time').apply(lambda x:x['close']/x.iloc0*x.iloc0)
temp=temp.reset_index(level=[0])
df['position']=temp['close']
df['position_max']=df['position']*df['high']/df['close']
df['position_min']=df['position']*df['low']/df['close']
##平仓时的仓位
#df.loc[close_pos_condition,'position']*=(1+df.loc[close_pos_condition,'sell_next_open_change'])
#计算持仓利润
df['porfit']=(df['position']-init_cash)*df['pos']#持仓利润或亏损
#df.loc[df['pos']==1,'porfit_min']=(df['position_min']-init_cash)*df['pos']#最小持仓盈利或亏损
#df.loc[df['pos']==0,'porfit_max']=(df['position_max']-init_cash)*df['pos']
#计算实际资金量
df['cash']=init_cash+df['porfit']#实际资金
#计算资金曲线
df['equity_change']=df['cash'].pct_change()
#开仓日收益率
df.loc[open_pos_condition,'equity_change']=df.loc[open_pos_condition,'cash']/init_cash-1
df['equity_change'].fillna(value=0,inplace=True)
df['equity_curve']=(1+df['equity_change']).cumprod()
df['equity_curve']=df['equity_curve']*init_cash
