import numpy as np
import inspect
import sys
import pydoc
try:
from . import Data_API
except:
import Data_API
def smart_index(obj,iter_index,default=None):
if(iter_index<0 or iter_index >= len(obj)):
return default
else:
return obj[iter_index]
def time_index(time_index_array,dt=None):
if dt == None:
dt = Data_API.Pricing_Database.pricing_date
index = np.searchsorted(time_index_array,dt)
if(index==len(time_index_array)):
return len(time_index_array)-1
elif time_index_array[index] == dt:
return index
else:
return index - 1
def is_biz_day(ticker,dt=None):
if dt==None:
dt = Data_API.Pricing_Database.pricing_date
cache = Data_API.Cache()
dates = cache.get_ticker_dates(ticker)
index = time_index(dates,dt)
if(dates[index] == dt):
return True
else:
return False
def price(ticker,date_offset=-1,price_feature='Close'):
"""
ticker: the underlyer ticker to price of
date_offset: int, default to -1, -k means the k-th last day as of observation
price_feature: default to 'Close', can use 'Open', 'Close', 'High', 'Low'
"""
cache = Data_API.Cache()
data = cache.get_ticker_data(ticker)
pricing_date_index = time_index(data.index.values)
if(date_offset<0):
date_offset = date_offset+1+pricing_date_index
elif date_offset>pricing_date_index:
date_offset = pricing_date_index
return data[price_feature][date_offset]
def prices(ticker,start=0,end=-1,price_feature='Close'):
cache = Data_API.Cache()
data = cache.get_ticker_data(ticker)
pricing_date_index = time_index(data.index.values)
if(start<0):
start = start+1+pricing_date_index
elif start>pricing_date_index:
start = pricing_date_index
if(end<0):
end = end+1+pricing_date_index
elif end>pricing_date_index:
end = pricing_date_index
return np.array(data[price_feature][start:end+1])
def price_return(obj, start=0, end=-1,price_feature='Close'):
"""
obj: the underlyer ticker to price of. Or it can be an numpy array
start: int, default to 0, -k means the k-th last day as of observation
end: int, default to -1, -k means the k-th last day as of observation
price_feature: default to 'Close', can use 'Open', 'Close', 'High', 'Low'
"""
if(isinstance(obj,str)):
obj = prices(obj,start,end,price_feature)
start = 0
end = -1
return obj[end]/obj[start] - 1.0
def log_price_return(obj, start=0, end=-1,price_feature='Close'):
"""
obj: the underlyer ticker to price of. Or it can be an numpy array
start: int, default to 0, -k means the k-th last day as of observation
end: int, default to -1, -k means the k-th last day as of observation
price_feature: default to 'Close', can use 'Open', 'Close', 'High', 'Low'
"""
if(isinstance(obj,str)):
obj = prices(obj,start,end,price_feature)
start = 0
end = -1
return np.log(obj[end]/obj[start])
def price_returns(obj, start=0, end=-1,price_feature='Close',step=1):
"""
obj: the underlyer ticker to price of. Or it can be an numpy array
start: int, default to 0, -k means the k-th last day as of observation
end: int, default to -1, -k means the k-th last day as of observation
price_feature: default to 'Close', can use 'Open', 'Close', 'High', 'Low'
"""
if(isinstance(obj,str)):
obj = prices(obj,start,end,price_feature)
start = 0
end = -1
if(end<0):
end += len(obj)
start_step = start + step
rets = np.array([obj[idx]/obj[idx-step]-1.0 for idx in range(start_step,end+1)])
return rets
def log_price_returns(obj, start=0, end=-1,price_feature='Close',step=1):
"""
obj: the underlyer ticker to price of. Or it can be an numpy array
start: int, default to 0, -k means the k-th last day as of observation
end: int, default to -1, -k means the k-th last day as of observation
price_feature: default to 'Close', can use 'Open', 'Close', 'High', 'Low'
"""
if(isinstance(obj,str)):
obj = prices(obj,start,end,price_feature)
start = 0
end = -1
if(end<0):
end += len(obj)
start_step = start + step
rets = np.log(np.array([obj[idx]/obj[idx-step] for idx in range(start_step,end+1)]))
return rets
def volatility(obj, start=0, end=-1,price_feature='Close'):
"""
obj: the underlyer ticker to price of. Or it can be an numpy array
start: int, default to 0, -k means the k-th last day as of observation
end: int, default to -1, -k means the k-th last day as of observation
price_feature: default to 'Close', can use 'Open', 'Close', 'High', 'Low'
"""
if(isinstance(obj,str)):
obj = prices(obj,start,end,price_feature)
start = 0
end = -1
if end == -1:
# end = -1 is a corner case here since (end+1) will be 0
end = len(obj) - 1
daily_change = np.log(obj[start+1:end+1]/obj[start:end])
std = np.std(daily_change)
vol = np.sqrt(Data_API.Pricing_Database.trading_days) * std
return vol
def max_draw_down(obj, start=0, end=-1, price_feature='Close'):
if isinstance(obj,str):
obj = prices(obj,start,end,price_feature)
start = 0
end = -1
if end == -1:
# end = -1 is a corner case here since (end+1) will be 0
end = len(obj) - 1
draw_downs = []
current_max = None
for i in range(start,end+1):
if current_max == None:
current_max = obj[i]+0.0
elif obj[i]>current_max:
current_max = obj[i]+0.0
draw_downs.append((obj[i]-current_max)/current_max)
max_d = np.min(draw_downs)
return min([0.0,max_d])
def draw_down(obj, start=0, end=-1, price_feature='Close'):
"""
obj: the underlyer ticker to price of. Or it can be an numpy array
start: int, default to 0, -k means the k-th last day as of observation
end: int, default to -1, -k means the k-th last day as of observation
price_feature: default to 'Close', can use 'Open', 'Close', 'High', 'Low'
"""
if(isinstance(obj,str)):
obj = prices(obj,start,end,price_feature)
start = 0
end = -1
if end == -1:
# end = -1 is a corner case here since (end+1) will be 0
end = len(obj) - 1
min_price = np.min(obj[start:end+1])
return min([0.0,(min_price - obj[start]) / obj[start]])
def sharpe(obj, start=0, end=-1,price_feature='Close'):
"""
obj: the underlyer ticker to price of. Or it can be an numpy array
start: int, default to 0, -k means the k-th last day as of observation
end: int, default to -1, -k means the k-th last day as of observation
price_feature: default to 'Close', can use 'Open', 'Close', 'High', 'Low'
"""
if(isinstance(obj,str)):
obj = prices(obj,start,end,price_feature)
start = 0
end = -1
if end <0:
end += len(obj)
ndays = end - start + 1
annual_return = (1 + price_return(obj,start,end)) ** (float(Data_API.Pricing_Database.trading_days) / ndays) - 1
vol = volatility(obj,start,end)
if annual_return == 0:
return 0
return annual_return / vol
def all_indicators_doc():
all_funcs = inspect.getmembers(sys.modules[__name__], inspect.isfunction)
ret = ""
for f in all_funcs:
ret += pydoc.render_doc(f[1], renderer = pydoc.plaintext).splitlines()[2] + "\n"
return ret
def average(obj, start=0, end=-1, price_feature='Close'):
if isinstance(obj, str):
obj = prices(obj, start, end, price_feature)
start = 0
end = -1
if end < 0:
end += len(obj)
if start < 0:
start += len(obj)
return np.mean(obj[start: (end + 1)])
def max(obj, start=0, end=-1, price_feature='Close'):
if isinstance(obj, str):
obj = prices(obj, start, end, price_feature)
start = 0
end = -1
if end < 0:
end += len(obj)
if start < 0:
start += len(obj)
return np.max(obj[start: (end + 1)])
def min(obj, start=0, end=-1, price_feature='Close'):
if isinstance(obj, str):
obj = prices(obj, start, end, price_feature)
start = 0
end = -1
if end < 0:
end += len(obj)
if start < 0:
start += len(obj)
return np.min(obj[start: (end + 1)])
def rsi(obj, start=-14, end=-1, price_feature='Close'):
if isinstance(obj, str):
obj = prices(obj, start, end, price_feature)
start = 0
end = -1
if end < 0:
end += len(obj)
if start < 0:
start += len(obj)
_data = np.diff(obj[start: (end + 1)])
len_gain = len(_data[_data > 0.0])
len_loss = len(_data[_data < 0.0])
if len_gain == 0 or len_loss == 0:
return 50
average_gain = np.mean(_data[_data > 0.0])
average_loss = np.abs(np.mean(_data[_data < 0.0]))
first_rs = average_gain / average_loss
rsi = 100 - 100 / (1 + first_rs)
return rsi
def correlation(obj1, obj2, start=0, end=-1, price_feature='Close'):
if isinstance(obj1, str) or isinstance(obj2, str):
obj1 = log_price_returns(obj1, start, end, price_feature)
obj2 = log_price_returns(obj2, start, end, price_feature)
# simple and rough treatment: assume biz days are the same among the two tickers
if len(obj1)>len(obj2):
obj1 = obj1[len(obj1)-len(obj2):]
else:
obj2 = obj2[len(obj2)-len(obj1):]
start = 0
end = -1
if end < 0:
end += len(obj1)
if start < 0:
start += len(obj1)
return np.corrcoef(obj1[start: (end + 1)], obj2[start: (end + 1)])[0, 1]
