2_Strategies/model/algo_sim_feed.py [15:100]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class AlgoSimData(DataBase): def __init__(self,datafile): super(AlgoSimData, self).__init__() df = pd.read_csv(datafile,infer_datetime_format=True, parse_dates=['dt']) start_date = '2012-08-13' end_date = '2017-08-11' now = datetime.datetime.now() # current date and time pred_end_date = now.strftime("%Y-%m-%d") self.fromdate=pd.to_datetime(end_date, format = "%Y-%m-%d") self.todate=pd.to_datetime(pred_end_date, format = "%Y-%m-%d") self.timeframe=bt.TimeFrame.Days S_eon = df[["dt","close"]] returns = (S_eon.loc[1:, 'close'] - \ S_eon.shift(1).loc[1:, 'close']) / \ S_eon.shift(1).loc[1:, 'close'] # Parameter Assignments So = S_eon.loc[S_eon.shape[0] - 1, "close"] dt = 1 # day # User input n_of_wkdays = pd.date_range(start = pd.to_datetime(end_date, format = "%Y-%m-%d") + pd.Timedelta('1 days'), end = pd.to_datetime(pred_end_date, format = "%Y-%m-%d")).to_series().map(lambda x: 1 if x.isoweekday() in range(1,6) else 0).sum() T = n_of_wkdays # days # User input -> follows from pred_end_date N = T / dt t = np.arange(1, int(N) + 1) mu = np.mean(returns) sigma = np.std(returns) scen_size = 1 # User input b = {str(scen): np.random.normal(0, 1, int(N)) for scen in range(1, scen_size + 1)} W = {str(scen): b[str(scen)].cumsum() for scen in range(1, scen_size + 1)} # Calculating drift and diffusion components drift = (mu - 0.5 * sigma**2) * t diffusion = {str(scen): sigma * W[str(scen)] for scen in range(1, scen_size + 1)} # Making the predictions S = np.array([So * np.exp(drift + diffusion[str(scen)]) for scen in range(1, scen_size + 1)]) S = np.hstack((np.array([[So] for scen in range(scen_size)]), S)) # add So to the beginning series # Dataframe format for predictions - first 10 scenarios only self.df = pd.DataFrame(S.swapaxes(0, 1)[:, :10]).set_index( pd.date_range(start = S_eon["dt"].max(), end = pred_end_date, freq = 'D').map(lambda x: x if x.isoweekday() in range(1, 6) else np.nan).dropna() ).reset_index(drop = False) print("SimData generated:from=%s,to=%s,count=%s" % (self.fromdate,self.todate,len(self.df))) self.n=0 def start(self): print("start feed") def stop(self): print("stop feed") def _load(self): #print("load feed") if self.n>=len(self.df): return False v=self.df.values #print(v) #print(self.n) dt=v[self.n][0] close=v[self.n][1] #print("%s:%s:%s" % (self.n,dt,close)) self.lines.datetime[0] = date2num(dt) print(self.num2date(self.lines.datetime[0])) self.lines.open[0] = close self.lines.high[0] = close self.lines.low[0] = close self.lines.close[0] = close self.lines.volume[0] = 0 self.n=self.n+1 return True - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 4_Kinesis/model/algo_sim_feed.py [15:100]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class AlgoSimData(DataBase): def __init__(self,datafile): super(AlgoSimData, self).__init__() df = pd.read_csv(datafile,infer_datetime_format=True, parse_dates=['dt']) start_date = '2012-08-13' end_date = '2017-08-11' now = datetime.datetime.now() # current date and time pred_end_date = now.strftime("%Y-%m-%d") self.fromdate=pd.to_datetime(end_date, format = "%Y-%m-%d") self.todate=pd.to_datetime(pred_end_date, format = "%Y-%m-%d") self.timeframe=bt.TimeFrame.Days S_eon = df[["dt","close"]] returns = (S_eon.loc[1:, 'close'] - \ S_eon.shift(1).loc[1:, 'close']) / \ S_eon.shift(1).loc[1:, 'close'] # Parameter Assignments So = S_eon.loc[S_eon.shape[0] - 1, "close"] dt = 1 # day # User input n_of_wkdays = pd.date_range(start = pd.to_datetime(end_date, format = "%Y-%m-%d") + pd.Timedelta('1 days'), end = pd.to_datetime(pred_end_date, format = "%Y-%m-%d")).to_series().map(lambda x: 1 if x.isoweekday() in range(1,6) else 0).sum() T = n_of_wkdays # days # User input -> follows from pred_end_date N = T / dt t = np.arange(1, int(N) + 1) mu = np.mean(returns) sigma = np.std(returns) scen_size = 1 # User input b = {str(scen): np.random.normal(0, 1, int(N)) for scen in range(1, scen_size + 1)} W = {str(scen): b[str(scen)].cumsum() for scen in range(1, scen_size + 1)} # Calculating drift and diffusion components drift = (mu - 0.5 * sigma**2) * t diffusion = {str(scen): sigma * W[str(scen)] for scen in range(1, scen_size + 1)} # Making the predictions S = np.array([So * np.exp(drift + diffusion[str(scen)]) for scen in range(1, scen_size + 1)]) S = np.hstack((np.array([[So] for scen in range(scen_size)]), S)) # add So to the beginning series # Dataframe format for predictions - first 10 scenarios only self.df = pd.DataFrame(S.swapaxes(0, 1)[:, :10]).set_index( pd.date_range(start = S_eon["dt"].max(), end = pred_end_date, freq = 'D').map(lambda x: x if x.isoweekday() in range(1, 6) else np.nan).dropna() ).reset_index(drop = False) print("SimData generated:from=%s,to=%s,count=%s" % (self.fromdate,self.todate,len(self.df))) self.n=0 def start(self): print("start feed") def stop(self): print("stop feed") def _load(self): #print("load feed") if self.n>=len(self.df): return False v=self.df.values #print(v) #print(self.n) dt=v[self.n][0] close=v[self.n][1] #print("%s:%s:%s" % (self.n,dt,close)) self.lines.datetime[0] = date2num(dt) print(self.num2date(self.lines.datetime[0])) self.lines.open[0] = close self.lines.high[0] = close self.lines.low[0] = close self.lines.close[0] = close self.lines.volume[0] = 0 self.n=self.n+1 return True - 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