-
Notifications
You must be signed in to change notification settings - Fork 390
/
trading_simulator.py
122 lines (100 loc) · 3.47 KB
/
trading_simulator.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
import pandas as pd
import numpy as np
import datetime as dt
import math
import warnings
warnings.filterwarnings("ignore")
prices = pd.read_csv("adjclose.csv", index_col="Date", parse_dates=True)
volumechanges = pd.read_csv("volume.csv", index_col="Date", parse_dates=True).pct_change()*100
today = dt.date(2000, 1, 15)
simend = dt.date(2019, 12, 31)
tickers = []
transactionid = 0
money = 1000000
portfolio = {}
activelog = []
transactionlog = []
def getprice(date, ticker):
global prices
return prices.loc[date][ticker]
def transaction(id, ticker, amount, price, type, info):
global transactionid
if type == "buy":
exp_date = today + dt.timedelta(days=14)
transactionid += 1
else:
exp_date = today
if type == "sell":
data = {"id": id, "ticker": ticker, "amount": amount, "price": price, "date": today, "type": type,
"exp_date": exp_date, "info": info}
elif type == "buy":
data = {"id": transactionid, "ticker": ticker, "amount": amount, "price": price, "date": today, "type": type,
"exp_date": exp_date, "info": info}
activelog.append(data)
transactionlog.append(data)
def buy(interestlst, allocated_money):
global money, portfolio
for item in interestlst:
price = getprice(today, item)
if not np.isnan(price):
quantity = math.floor(allocated_money/price)
money -= quantity*price
portfolio[item] += quantity
transaction(0, item, quantity, price, "buy", "")
def sell():
global money, portfolio, prices, today
itemstoremove = []
for i in range(len(activelog)):
log = activelog[i]
if log["exp_date"] <= today and log["type"] == "buy":
tickprice = getprice(today, log["ticker"])
if not np.isnan(tickprice):
money += log["amount"]*tickprice
portfolio[log["ticker"]] -= log["amount"]
transaction(log["id"], log["ticker"], log["amount"], tickprice, "sell", log["info"])
itemstoremove.append(i)
else:
log["exp_date"] += dt.timedelta(days=1)
itemstoremove.reverse()
for elem in itemstoremove:
activelog.remove(activelog[elem])
def simulation():
global today, volumechanges, money
start_date = today - dt.timedelta(days=14)
series = volumechanges.loc[start_date:today].mean()
interestlst = series[series > 100].index.tolist()
sell()
if len(interestlst) > 0:
#moneyToAllocate = 500000/len(interestlst)
moneyToAllocate = currentvalue()/(2*len(interestlst))
buy(interestlst, moneyToAllocate)
def getindices():
global tickers
f = open("symbols.txt", "r")
for line in f:
tickers.append(line.strip())
f.close()
def tradingday():
global prices, today
return np.datetime64(today) in list(prices.index.values)
def currentvalue():
global money, portfolio, today, prices
value = money
for ticker in tickers:
tickprice = getprice(today, ticker)
if not np.isnan(tickprice):
value += portfolio[ticker]*tickprice
return int(value*100)/100
def main():
global today
getindices()
for ticker in tickers:
portfolio[ticker] = 0
while today < simend:
while not tradingday():
today += dt.timedelta(days=1)
simulation()
currentpvalue = currentvalue()
print(currentpvalue, today)
today += dt.timedelta(days=7)
main()