def GenBTReport(bt_file_path, file_name='strat_pnl_hist'):
r = Reader()
t = Trader()
r.load_order_file(bt_file_path)
for i in range(r.get_ordersize()):
o = r.read_border(i)
if o.price > 0 and abs(o.size) > 0:
t.RegisterOneTrade(o.ticker, o.size if o.side == 1 else -o.size,
o.price)
t.PlotStratPnl(file_name)
return t.GenDFReport(), t.GenStratReport()
class BackTestor:
def __init__(self, order_path):
self.r = Reader()
self.t = Trader()
self.LoadOrder(order_path)
def LoadOrder(self, order_path):
self.r.load_order_file(order_path)
for i in range(self.r.get_ordersize()):
o = self.r.read_border(i)
if o.price > 0:
self.t.RegisterOneTrade(o.contract, o.size if o.side == 1 else -o.size, o.price)
def Plot(self):
self.t.PlotStratPnl()
def TradeReport(date_prefix, trade_path, cancel_path, file_name=''):
trader = Trader()
command = 'cat ' + date_prefix + 'log/order.log | grep Filled > ' + trade_path + '; cat ' + date_prefix + 'log/order_night.log | grep Filled >> ' + trade_path
command_result = subprocess.Popen(command,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
command = 'cat ' + date_prefix + 'log/order.log | grep Cancelled > ' + cancel_path + '; cat ' + date_prefix + 'log/order_night.log | grep Cancelled >> ' + cancel_path
command_result = subprocess.Popen(command,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
time.sleep(3)
trade_details = []
with open(trade_path) as f:
ei = ExchangeInfo()
for l in f:
temp = []
ei.construct(l)
temp.append(
datetime.datetime.fromtimestamp(float(
ei.time_str)).strftime("%Y-%m-%d %H:%M:%S"))
temp.append(ei.ticker)
temp.append("Buy" if ei.side == 0 else "Sell")
temp.append(ei.trade_price)
temp.append(ei.trade_size)
trade_details.append(temp)
trader.RegisterOneTrade(
ei.ticker,
int(ei.trade_size) if ei.side == 0 else -int(ei.trade_size),
float(ei.trade_price))
#print('printint')
df = trader.GenDFReport()
trader.PlotStratPnl(file_name=file_name)
#print(df)
#trader.Summary()
df.insert(len(df.columns), 'cancelled', 0)
with open(cancel_path) as f:
ei = ExchangeInfo()
for l in f:
ei.construct(l)
if ei.ticker not in df.index:
df.loc[ei.ticker] = 0
df.loc[ei.ticker, 'cancelled'] = df.loc[ei.ticker, 'cancelled'] + 1
return df, trader.GenStratReport(), pd.DataFrame(
trade_details, columns=['time', 'ticker', 'Side', 'price', 'size'])
class BaseFactor:
def __init__(self):
self.f_value = []
self.tr = Trader()
def run(self, start_date, end_date, ticker):
self.m = self.LoadData(start_date, end_date, ticker)
self.CalFactor()
self.PlotFactor()
self.TestPnl()
self.PlotSignal()
def PlotSignal(self):
df_list = [i[1] for i in sorted(self.m.items(), key=lambda x: x[0])]
if len(df_list) < 1:
print('empty df')
return
df = df_list[0]
start = time.time()
for i in range(1, len(df_list)):
df = pd.merge(df, df_list[i], how='outer')
print('finished merge used %lfs' % (time.time() - start))
plt.plot(df['mid'], label='mid', alpha=0.3)
buy_x = df[df['money'] < 0].index.tolist()
buy = df[df['money'] < -0.1]
plt.scatter(x=buy.index.tolist(),
y=buy['mid'].tolist(),
marker='.',
s=[4] * len(buy),
c='red',
label='buy')
sell = df[df['money'] > 0.1]
plt.scatter(x=sell.index.tolist(),
y=sell['mid'].tolist(),
marker='.',
s=[4] * len(sell),
c='green',
label='sell')
plt.title('factor percentile signal')
plt.legend()
plt.grid()
plt.show()
def TestPnl(self):
for k in self.m:
df = self.m[k]
up = df['factor'].quantile(0.999)
down = df['factor'].quantile(0.001)
df['money'] = np.where(df['factor'] > up, df['mid'], 0.0)
df['money'] = np.where(df['factor'] < down, -df['mid'],
df['money'])
for i in df['money'].tolist():
if abs(i) > 1:
self.tr.RegisterOneTrade('ni8888', 1 if i > 0 else -1,
abs(i))
self.tr.Summary()
self.tr.PlotStratRawPnl(show=True)
self.tr.PlotStratPnl(show=True)
def PlotFactor(self):
plt.plot(self.f_value, label='factor value')
plt.title('factor value curve')
plt.legend()
plt.grid()
plt.show()
def CalFactor(self):
for k in sorted(self.m.keys()):
df = self.m[k]
df['factor'] = self.cal(df)
self.f_value += df['factor'].tolist()
@abstractmethod
def cal(self, df):
pass
def LoadData(self, start_date, end_date, ticker):
dl = dateRange(start_date, end_date)
m = {}
for date in dl:
path = '/root/' + date + '/' + ticker + '.csv'
if os.path.exists(path):
m[date] = self.ReadData(date, ticker)
else:
print("%s not existed!" % (path))
return m
def ReadData(self, date, ticker):
path = '/root/' + date + '/' + ticker + '.csv'
df = pd.read_csv(path, header=None)
df.columns = shot.get_columns()
df['mid'] = (df['asks[0]'] + df['bids[0]']) / 2
df['return1'] = df['mid'].diff(1).fillna(0.0) / df['mid']
return df