From 7c1eed838102ab210f228bb24e98a240a40f7c83 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, 12 May 2024 23:24:12 +0000
Subject: [PATCH] Update ec2.py
---
ec2.py | 65 +++++++++++++++++++++++++++++-----------------------------
1 file changed, 32 insertions(+), 33 deletions(-)
diff --git a/ec2.py b/ec2.py
index b59d3be..4bfea8a 100644
--- a/ec2.py
+++ b/ec2.py
@@ -4,62 +4,61 @@ import math, random, sys, json
from statistics import mean, stdev
-event = json.loads(sys.stdin.read())
+session = json.loads(sys.stdin.read())
-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']
+date = eval(session['key1'])
+close = eval(session['key2'])
+buy = eval(session['key3'])
+sell = eval(session['key4'])
+h = int(session['key5'])
+d = int(session['key6'])
+t = session['key7']
minhistory = h
shots = d
-var95_list = []
-var99_list = []
+var_95_list = []
+var_99_list = []
dates = []
for i in range(minhistory, len(close)):
- if t == "buy":
- if buy[i] == 1: # if we’re interested in Buy signals
+ if t == "sell":
+ if sell[i] == 1: # if we’re interested in Sell signals
close_data = close[i-minhistory:i]
- pct_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1,len(close_data))]
- mn = mean(pct_change)
- std = stdev(pct_change)
+ percent_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1,len(close_data))]#Calculating percenage change as dataframe method pct_change can't be used
+ mean = mean(percent_change)
+ std = stdev(percent_change)
# generate much larger random number series with same broad characteristics
- simulated = [random.gauss(mn,std) for x in range(shots)]
+ simulated = [random.gauss(mean,std) for x in range(shots)]
# sort and pick 95% and 99% - not distinguishing long/short risks here
simulated.sort(reverse=True)
var95 = simulated[int(len(simulated)*0.95)]
var99 = simulated[int(len(simulated)*0.99)]
- var95_list.append(var95)
- var99_list.append(var99)
- dates.append(str(dt[i]))
- elif t == "sell":
- if sell[i] == 1: # if we’re interested in Sell signals
+ var_95_list.append(var95)
+ var_99_list.append(var99)
+ dates.append(str(date[i]))
+ if t == "buy":
+ if buy[i] == 1: # if we’re interested in Buy signals
close_data = close[i-minhistory:i]
- pct_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1,len(close_data))]
- mn = mean(pct_change)
- std = stdev(pct_change)
+ percent_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1,len(close_data))]#Calculating percenage change as dataframe method pct_change can't be used
+ mean = mean(percent_change)
+ std = stdev(percent_change)
# generate much larger random number series with same broad characteristics
- simulated = [random.gauss(mn,std) for x in range(shots)]
+ simulated = [random.gauss(mean,std) for x in range(shots)]
# sort and pick 95% and 99% - not distinguishing long/short risks here
simulated.sort(reverse=True)
var95 = simulated[int(len(simulated)*0.95)]
var99 = simulated[int(len(simulated)*0.99)]
- var95_list.append(var95)
- var99_list.append(var99)
- dates.append(str(dt[i]))
+ var_95_list.append(var95)
+ var_99_list.append(var99)
+ dates.append(str(date[i]))
output = {"dates" : dates,
- "var95" : var95_list,
- "var99" : var99_list
+ "var95" : var_95_list,
+ "var99" : var_99_list
}
-output = json.dumps(output)
+final_output = json.dumps(output)
print("Content-Type: application/json")
print()
-print(output)
-
+print(final_output)
--
GitLab