1.
ELW 수사를 하면서 스캘핑은 누구나 아는 명사가 되었습니다. Investopedia은 스캘핑을 다음과 같이 정의합니다.
Traders who implement this strategy are known as scalpers. The main goal is to buy (or sell) a number of shares at the bid (or ask) price and then quickly sell them a few cents higher (or lower) for a profit. Many small profits can easily compound into large gains if a strict exit strategy is used to prevent large losses.
혹은
당일 중의 짧은 시간 동안에 시세변동을 이용하여 이익을 실현하고자 하는 초단기 거래자
고빈도매매(HFT)에서 스캘핑전략은 어떨까요? 오래전 HFT를 바라보는 이런저런 시각에서 소개했던 Introduction To HFT Scapling Strategies는 아래 요소들이 결합되어야 한다고 하였습니다.
High Frequency Turnover – passive scalping of a micro-spread
Queue Position – a dependence on order rank and order book depth
Low Latency – precise and timely reaction to market microstructure events
Exchange Microstructure – usage of special order types and order matching engine features
Rebate Capture – subsidized costs through “post only” orders and tiered rebates
Low Risk Tolerance – avoidance of risk and usage of market book depth to reduce risk
위 논문에 따르면 고빈도 스캘핑전략은 전통적인 스캘핑과 다른 방법으로 알파를 얻는다고 합니다. 즉, 가격의 변화를 예측하는 것은 아니라고 합니다. 물론 한국과 차이가 있습니다.
To make it profitable, there must be some structural advantage (alpha) in addition to the basic order book depth asymmetry signals and execution tactics. The HFT scalpers’ alpha is not a traditional prediction of market movement; it is an “effective alpha” obtained through the exploitation of market technologies (i.e. exchange “plumbing”) and market microstructure. HFT scalping methods that leveraged the precursor to spam-and-cancel strategies, using post only orders (order types that
discriminate against order flow by trading only against marketable orders willing to pay taker fees), gradually gained market share before the adaptation of Regulation NMS in 2007.
2.
위의 논문이 고빈도 스캘핑전략의 특징을 정리한 것이라고 하면 The Mathematics of Scalping은 제목처럼 스캘핑전략을 수학적으로 분석합니다.(^^)
I will define a scalping strategy as one in which we seek to take small profits by posting limit orders on alternate side of the book. Scalping, as I define it, is a strategy rather like market making, except that we “lean” on one side of the book. So, at any given time, we may have a long bias and so look to enter with a limit buy order. If this is filled, we will then look to exit with a subsequent limit sell order, taking a profit of a few ticks. Conversely, we may enter with a limit sell order and look to exit with a limit buy order.
The strategy relies on two critical factors:(i) the alpha signal which tells us from moment to moment whether we should prefer to be long or short
(ii) the execution strategy, or “trade expression”
저자가 ii)를 주로 다루면서 두가지 방법을 이야기합니다. Latency와 Layering입니다.
Layering is a strategy in high-frequency trading where a brokerage firm makes and then cancels orders that they never intended to carry out.
그리고 위험관리를 위한 Exit 방법을 소개하고 모델화합니다.
WinRate[currentPrice_,annualVolatility_,BarSizeMins_, nTicksPT_, nTicksSL_,minMove_, tickValue_, costContract_]:=Module[{ nMinsPerDay, periodVolatility, tgtReturn, slReturn,tgtDollar, slDollar, probWin, probLoss, winRate, expWinDollar, expLossDollar, expProfit},
nMinsPerDay = 250*6.5*60;
periodVolatility = annualVolatility / Sqrt[nMinsPerDay/BarSizeMins];
tgtReturn=nTicksPT*minMove/currentPrice;tgtDollar = nTicksPT * tickValue;
slReturn = nTicksSL*minMove/currentPrice;
slDollar=nTicksSL*tickValue;
probWin=1-CDF[NormalDistribution[0, periodVolatility],tgtReturn];
probLoss=CDF[NormalDistribution[0, periodVolatility],slReturn];
winRate=probWin/(probWin+probLoss);
expWinDollar=tgtDollar*probWin;
expLossDollar=slDollar*probLoss;
expProfit=expWinDollar+expLossDollar-costContract;
{expProfit, winRate}]
나머지는 독자의 몫입니다.
위 논문은 CDF형식으로 만들어졌지만 아직 올라오지 않았습니다. 나중 CDF가 올라오면 따로 소개하도록 하겠습니다.
위 파일을 보려면 Wolfram CDF player 과 plug-in을 설치하셔야 합니다. 약간 수고스럽더라도 설치해서 살펴보시길 바랍니다.
