def preprocess(universe, from_date, to_date):
scripts = list_dict_to_list(select_companies(universe))
# scripts = ['MINDTREE']
data = {}
parent_price = adjust_values(parent_company, from_date, to_date)
data["Parent Price"] = parent_price
data["Price"] = {}
data["High Low"] = {}
data["Sma"] = {}
data["Bar Width"] = {}
data["Volume"] = {}
data["Tmf"] = {}
viable_scripts = []
for script in scripts:
try:
data["Price"][script] = adjust_values(script, from_date, to_date)
if len(data["Price"][script]) == 0:
continue
# TODO code 1, 2, 3 month 200 day offset to check upward slope
# TODO volume data
temp_sma_200 = price_sma(script, from_date, to_date, 200)
temp_sma_150 = price_sma(script, from_date, to_date, 150)
temp_sma_50 = price_sma(script, from_date, to_date, 50)
data["Sma"][script] = pd.concat([
temp_sma_200["Close"], temp_sma_200["200 MA"],
temp_sma_150["150 MA"], temp_sma_50["50 MA"]
],
axis=1)
data["High Low"][script] = high_low(script, from_date, to_date)
temp_bar_width = bar_width(script, from_date, to_date)
bar_sma_50 = indicator_sma(temp_bar_width, 50)
bar_sma_21 = indicator_sma(temp_bar_width, 21)
bar_sma_10 = indicator_sma(temp_bar_width, 10)
data["Bar Width"][script] = pd.concat([
temp_bar_width["Indicator"], bar_sma_50["50 MA"],
bar_sma_21["21 MA"], bar_sma_10["10 MA"]
],
axis=1)
temp_sma_50 = volume_sma(script, from_date, to_date, days=50)
temp_sma_10 = volume_sma(script, from_date, to_date, days=10)
data["Volume"][script] = pd.concat([
temp_sma_50["Volume"], temp_sma_50["50 MA"],
temp_sma_10["10 MA"]
],
axis=1)
data["Tmf"][script] = tmf(script, from_date, to_date, days=21)
viable_scripts.append(script)
print(len(viable_scripts))
except:
input(script)
raise
scripts = viable_scripts
return data, viable_scripts
from datetime import datetime
import pandas as pd
import _pickle as pickle
import os
# global
sl_multiplier = 0.85
slippage_multiplier = 0.01
from_date = datetime(2012, 1, 1)
to_date = datetime(2019, 1, 1)
family = 'nse500'
parent_company = 'LUPIN'
parent_price = adjust_values(parent_company, from_date, to_date)
scripts = list_dict_to_list(select_companies(family))
# scripts = ["ACC"]
########################################################################################
# these functions will be unique for strat. Need to work on how to do this
def hit_sl(pos, date):
global data
correct_date = get_closest_date(date, data["Price"][pos["Script"]])
if (float(data["Price"][pos["Script"]].loc[correct_date, "Close"]) <
pos["SL"]):
return True
return False