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powered by NetLogo
view/download model file: stop-loss_strategy.nlogo
It is a simulation of a Stop-Loss strategy.
We wanted to compare the stop loss strategy with a winning random strategy. In order to make the random strategy winning we made random investors start with a long position in stocks. Plus, we gave random agents the possibility to purchase and sell at the same time, creating an asimmetry in the market.
The comparison with a pure random strategy would not have been significant since the stop-loss strategy tends to gain by costruction.
A new price is continuously formed in the market by Random Agents.
The Stop-Loss Strategy starts after 52 weeks, when also the StopLossAgents enter in the market.
Choose the number of Random Agents and Stop-loss Agents.
The strategy without transaction costs has always a gain.
Look at the gain of Stop-Loss Agents with different parameters, like checking time, shock, transaction costs, seed.
It would be interesting implement different strategies in order to compare the different performances.
Since the cumulative distribution function of a Gaussian is not implemented in Netlogo by default, we exploited the job of an external we found on internet.
The link is the following:
http://stackoverflow.com/questions/809362/cumulative-normal-distribution-in-python
Our model starts from the following:
http://eco83.econ.unito.it/terna/simoec13/NetLogo_examples/
g1_CDA_basic_model.nlogo
This model was created as a project work for the course of “Simulation Models for Economics” 2012-2013 by Professor Pietro Terna, School of Management and Economics, University of Turin
Pietro Terna: http://web.econ.unito.it/terna/
; Unlike most things you buy, both the buyer and seller set stock prices.
; The buyer states what price they will pay for the stock: this is the bid price.
; The seller also has a price: the ask price.
; If you have access to the proper online pricing systems, you can see the bid and
; ask prices. You will notice that the bid price and the ask price are never the same.
; The ask price is always a little higher than the bid price.
; What this means is if you are buying the stock you pay the ask price (the higher
; price) and if you are selling the stock you receive the bid price (the lower price).
;from http://stocks.about.com/od/tradingbasics/a/bidask101704.htm
breed [randomAgents randomAgent]
breed [slAgents slAgent]
randomAgents-own[buy sell pass price randomPtf randomBuy randomSell]
slAgents-own[buy sell pass price strike d1 d2 Nd1 Nd2 slPtf naked covered]
globals [logB logS exePrice out-of-market priceVector logVector sigma checkStrategy j k p n BScall]
to setup
clear-all
if seed != 0 [random-seed seed]
set exePrice 20000
set logB []
set logS []
set priceVector []
set logVector []
set k 1
reset-ticks
create-randomAgents nRandomAgents
create-slAgents nslAgents
ask randomAgents
[
set shape "person"
set out-of-market False
set size 2
set randomBuy 1
set randomSell 0
]
ask SLAgents
[
set shape "person"
set out-of-market False
set buy false
set sell false
set size 2
set strike 20000
set covered false
set naked false
set pass false
]
end
to go
if check = "hour" [set checkStrategy 35 ] if check = "week" [set checkStrategy 1 ] if check = "month" [set checkStrategy 0.25 ]
ask randomAgents
[
ifelse out-of-market [set color white]
[ifelse random-float 1 < passLevel [set pass True][set pass False]
ifelse not pass
[ifelse random-float 1 < 0.5 [set buy True set sell False]
[set sell True set buy False] ]
[set buy False set sell False]
;set price 501 + random 999
;set price random-normal 1000 100
set price exePrice + (random-normal 0 shock)
]
]
;use these clearing operationa if a 'go cycle' is 'a day'
;commenting them logs are kept until the end of the simulation experiment
set logB []
set logS []
tick
;show ticks
;NB in future implementation it would be interesting to make empty both the side of
; the market at regular intervals of more than one cycle
ask turtles
[if not pass and not out-of-market
[
let tmp[]
set tmp lput price tmp
set tmp lput who tmp
if buy [set logB lput tmp logB]
set logB reverse sort-by [item 0 ?1 < item 0 ?2] logB
;show logB
if (not empty? logB and not empty? logS) and
item 0 (item 0 logB) >= item 0 (item 0 logS)
[set exePrice item 0 (item 0 logS)
set j j + 1 set n n + 1
set k k + 1
;strategy based on random market prices: SELL
set priceVector fput exePrice priceVector
if length priceVector > 1
[let tmp2 ln ( item 0 priceVector / item 1 priceVector )
set logVector fput tmp2 logVector]
if length logVector > 700
[set logVector butlast logVector
set sigma standard-deviation (logVector) * sqrt (36400)
]
if k = 700 * T [ ask SLAgents[
set strike exePrice + (random-float 2 - 1) * 200
set d1 (ln (exeprice / strike) + (risk-free + ((sigma ^ 2) / 2)) * T) / ( sigma * sqrt ( T ))
set d2 d1 - sigma * sqrt (T) ;show d1
;show d2
let a d1 / (2 ^ 0.5)
let b d2 / (2 ^ 0.5)
set Nd1 1 - 0.5 * erfcc a
set Nd2 1 - 0.5 * erfcc b
set BScall exePrice * Nd1 - strike * exp( - risk-free * T ) * Nd2
ifelse exePrice < strike
[set slPtf slPtf + BScall * (1 - TransactionCost) set naked true set covered false]
[set slPtf slPtf + BScall * (1 - TransactionCost) - exePrice * (1 + TransactionCost)
set covered true set naked false set price exePrice set buy true set sell false]
]
set k 0
set p p + 1
]
if j = 700 / checkStrategy [ ask SLAgents[
if naked and exeprice > strike
[
set covered true set naked false set slPtf slPtf - exePrice * (1 + TransactionCost)
set price strike set buy true set sell false
]
if covered and exeprice < strike
[
set naked true set covered false set slPtf slPtf + exeprice * (1 - TransactionCost)
set price strike set sell true set buy false
]
]
set j 0
]
if k = 700 * (T - 1) and p > 0[ ask SLAgents[
if exePrice > strike
[
set slPtf slPtf + strike * (1 - TransactionCost)
]
]
]
let agB item 1 (item 0 logB)
let agS item 1 (item 0 logS)
ask randomAgents [
if randomBuy >= 1 and random-float 1 < .5 [set randomPtf randomPtf + exeprice
set randomSell randomSell + 1
set randomBuy randomBuy - 1]]
set logB but-first logB
set logS but-first logS
]
;show exePrice
if sell [set logS lput tmp logS]
set logS sort-by [item 0 ?1 < item 0 ?2] logS
;show logS
if (not empty? logB and not empty? logS) and
item 0 (item 0 logB) >= item 0 (item 0 logS)
[ set exePrice item 0 (item 0 logB)
set j j + 1 set n n + 1
set k k + 1
;strategy based on random market prices: BUY
set priceVector fput exePrice priceVector
if length priceVector > 1
[let tmp2 ln ( item 0 priceVector / item 1 priceVector )
set logVector fput tmp2 logVector]
if length logVector > 700
[set logVector butlast logVector set sigma standard-deviation (logVector) * sqrt (36400)
]
if k = 700 * T [ ask SLAgents[
set strike exePrice + (random-float 2 - 1) * 200
set d1 (ln (exeprice / strike) + (risk-free + ((sigma ^ 2) / 2)) * T) / ( sigma * sqrt ( T))
set d2 d1 - sigma * sqrt (T) ;show d1
;show d2
let a d1 / (2 ^ 0.5)
let b d2 / (2 ^ 0.5)
set Nd1 1 - 0.5 * erfcc a
set Nd2 1 - 0.5 * erfcc b
set BScall exePrice * Nd1 - strike * exp( - risk-free * T ) * Nd2
ifelse exePrice < strike
[set slPtf slPtf + BScall * (1 - TransactionCost) set naked true set covered false]
[set slPtf slPtf + BScall * (1 - TransactionCost) - exePrice * (1 + TransactionCost)
set covered true set naked false set price exePrice set buy true set sell false]
]
set k 0
set p p + 1
]
if j = 700 / checkStrategy [ ask SLAgents[
if naked and exeprice > strike
[
set covered true set naked false set slPtf slPtf - exePrice * (1 + TransactionCost)
set price strike set buy true set sell false
]
if covered and exeprice < strike
[
set naked true set covered false set slPtf slPtf + exeprice * (1 - TransactionCost)
set price strike set sell true set buy false
]
]
set j 0
]
if k = 700 * (T - 1) and p > 0[ ask SLAgents[
if exePrice > strike
[
set slPtf slPtf + strike * (1 - TransactionCost)
]
]
]
let agB item 1 (item 0 logB)
let agS item 1 (item 0 logS)
ask randomAgents [
if randomSell >= 1 and random-float 1 < .5 [set randomPtf randomPtf - exeprice
set randomSell randomSell - 1
set randomBuy randomBuy + 1]]
set logB but-first logB
set logS but-first logS
]
;show exePrice
]
if random-float 1 < out-of-marketLevel
[if exePrice > 1500 [set out-of-market False]
if exePrice < 500 [set out-of-market True]
]
graph
]
end
;; Complementary error function
to-report erfcc [x]
let z abs x
let q 1.0 / (1.0 + 0.5 * z)
let r q * exp ( - z * z - 1.26551223 + q * (1.00002368 + q * (0.37409196 +
q * (0.09678418 + q * ( - 0.18628806 + q * (0.27886807 +
q * ( - 1.13520398 + q * (1.48851587 + q * ( - 0.82215223 +
q * 0.17087277 )))))))))
ifelse (x >= 0) [ report r ] [report 2.0 - r]
end
to graph
set-current-plot "exePrice"
plot exePrice
end