From d4ef1cde37c6d94c2dd5fac7cef33de1c36cdc44 Mon Sep 17 00:00:00 2001 From: "Madhavan Pillai Sasidharan Nai, Meenu (PG/T - Comp Sci & Elec Eng)" <mm03943@surrey.ac.uk> Date: Sun, 19 May 2024 19:56:28 +0000 Subject: [PATCH] Update ec2.py --- ec2.py | 41 +++++++++++++++++++++-------------------- 1 file changed, 21 insertions(+), 20 deletions(-) diff --git a/ec2.py b/ec2.py index ea2b56f..d174183 100644 --- a/ec2.py +++ b/ec2.py @@ -1,29 +1,30 @@ #!/usr/bin/python3 -from statistics import mean, stdev import math, random, sys, json +from statistics import mean, stdev -args = sys.stdin.read() -session = json.loads(args) +event = json.loads(sys.stdin.read()) -date = eval(session['key1']) -close = eval(session['key2']) -buy = eval(session['key3']) -sell = eval(session['key4']) -minhistory = int(session['key5']) -shots = int(session['key6']) -t = session['key7'] +dt = eval(event['key1']) +close = eval(event['key2']) +buy = eval(event['key3']) +sell = eval(event['key4']) +h = int(event['key5']) +d = int(event['key6']) +t = event['key7'] +minhistory = h +shots = d var_95_list = [] var_99_list = [] dates = [] for i in range(minhistory, len(close)): - if t == "sell": - if sell[i] == 1: #If it is to sell + if t == "buy": + if buy[i] == 1: #If it is to buy closing = close[i-minhistory:i] - percent_change = [(closing[i] - closing[i-1]) / closing[i-1] for i in range(1,len(closing))]#Calculating percenage change as dataframe method pct_change can't be used + percent_change = [(closing[i] - closing[i-1]) / closing[i-1] for i in range(1,len(closing))] mean_value = mean(percent_change) std_value = stdev(percent_change) # generate much larger random number series with same broad characteristics @@ -34,11 +35,11 @@ for i in range(minhistory, len(close)): var99 = simulated[int(len(simulated)*0.99)] var_95_list.append(var95) var_99_list.append(var99) - dates.append(str(date[i])) - if t == "buy": - if buy[i] == 1: #If it is to buy + dates.append(str(dt[i])) + elif t == "sell": + if sell[i] == 1: #If it is to sell closing = close[i-minhistory:i] - percent_change = [(closing[i] - closing[i-1]) / closing[i-1] for i in range(1,len(closing))]#Calculating percenage change as dataframe method pct_change can't be used + percent_change = [(closing[i] - closing[i-1]) / closing[i-1] for i in range(1,len(closing))] mean_value = mean(percent_change) std_value = stdev(percent_change) # generate much larger random number series with same broad characteristics @@ -49,15 +50,15 @@ for i in range(minhistory, len(close)): var99 = simulated[int(len(simulated)*0.99)] var_95_list.append(var95) var_99_list.append(var99) - dates.append(str(date[i])) + dates.append(str(dt[i])) output = {"dates" : dates, "var95" : var_95_list, "var99" : var_99_list } -final_output = json.dumps(output) +output = json.dumps(output) print("Content-Type: application/json") print() -print(final_output) +print(output) -- GitLab