From 31dff4cf358c598ab20b7e6583961b639a2dc68a 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, 5 May 2024 13:52:03 +0000
Subject: [PATCH] Upload New File
---
ec2.py | 65 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 65 insertions(+)
create mode 100644 ec2.py
diff --git a/ec2.py b/ec2.py
new file mode 100644
index 0000000..b59d3be
--- /dev/null
+++ b/ec2.py
@@ -0,0 +1,65 @@
+#!/usr/bin/python3
+
+import math, random, sys, json
+from statistics import mean, stdev
+
+
+event = 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']
+minhistory = h
+shots = d
+var95_list = []
+var99_list = []
+dates = []
+
+for i in range(minhistory, len(close)):
+ 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)
+ # generate much larger random number series with same broad characteristics
+ simulated = [random.gauss(mn,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
+ 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)
+ # generate much larger random number series with same broad characteristics
+ simulated = [random.gauss(mn,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]))
+
+output = {"dates" : dates,
+ "var95" : var95_list,
+ "var99" : var99_list
+ }
+
+output = json.dumps(output)
+
+print("Content-Type: application/json")
+print()
+print(output)
+
--
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