• Jan

Position sizing, R (risk) units

Updated: May 6



Consistency in trading begins with money management, and one of the critical aspects is correctly sizing positions. The aim of a trader should be for each trade to have a similar % impact on account balance as much as possible. With experience, the trader will know which trades to scale more aggressively and which to use the weaker size of the position. Still, initially, especially on the setups where the trader does not have a firm conviction, it is best practice to always use a similar % risk on each trade.

Two components go into sizing the positions and exposing the account that is traded, one is the strength of volatility on asset (outside exposure), and the other is the % of account equity risk (inside exposure) of how much % of account balance (in USD) trader wants to expose on each trade. Together, both variables will dictate how to scale / size positions and define the R unit on trade properly. By excluding either of those two variables, the R units are no longer neutralized, meaning that the component of randomness is increased.

The trader's goal should be to have limited % risk on each trade where the edge comes through over a decent sample size of trades, and there is enough room for several consecutive losses so that account is not destroyed or drained too much before the edge turns equity balance back into a neutral or positive state.

This means the % of risk will depend on many different things. It is often stated that traders should risk 1 or 2% per position maximum which is often just a general number thrown out there. It is a good basic guide don't get me wrong, but let's break down more in-depth on how to determine how much you should risk per trade because a 2% risk might be too conservative or too aggressive for one, depending on the factors.



How much to risk per trade in terms of % of equity?





To define ideal % of risk-on trade, there are several basic factors to determine that (the factors bellow significantly impact how much trader can afford to risk in terms of the account balance in USD):

-leverage / margin used on traded market / asset

-borrows costs (if trading hard to borrow assets)

-the edge on play/pattern in terms of avg drawdown and win rate

-average R return on the traded pattern (1, 2,3, or more R)

-appetite for risk and equity curve on account

Each of those components has to be given proper thought for each trader to calculate and determine how does the variable play into the % risk room that the trader can afford on trade.

Leverage:


High leverage can allow the trader to risk a higher % of account since less margin will be used to capitalize the trade, while less leverage might force the trader to risk less % per trade since more of the account balance goes to cover the margin on trade. High leverage is not necessarily a bad thing by itself, since higher leverage allows trader to have more spare capital to position into further trades at the same time while still carrying the initial trade. It also keeps trader further away from margin call as less margin is used on position, however all of that of course under assumption that trader has not oversized the position. High levareage used does not equal heavy sizing of the position by itself. Those two are not the same, very often confused within trading industry.

Borrow costs:

Markets or assets with very expensive borrows on small-cap equities can impact the additional cost per trade substantially especially on smaller trading accounts. It is an additional extra cost to that initially planned % risk per trade if one trades those assets frequently. It's a necessary evil but it is as well important to add it to a cost basis and it might impact the minimal size that trader has to take on a position in order to get above the borrow cost on trade.

Win / loss rate and avg dradown on setup:


Edge on play has several metrics that allow traders for larger risk or lower risk on trade. This involves components such as drawdown and average win rate as the two most common variables. The higher the win rate on a setup (over 100 samples), the more % risk can trader expose his / her account on a trade. And as for drawdown, the less drawdown that happens on the trading account over a period of 30 trades the more % risk can trader put on a trade. Trading strategies or patterns that have large swings of drawdown in the equity curve over a period of 30 or more trades require a trader to be much more conservative on risk % per trade. Otherwise, one bad slump period of 30 trades can destroy the account.

Risk appetite:


Appetite for risk will also determine how much traders should risk. Theoretically, this should not affect the risk per trade at all, as it is outside of the rule-book variable (it is highly subjective and not necessarily guided by patterns data behavior), but in the real practical world, it is something that has a substantial impact due to many psychological or capital income factors for each individual. Usually, traders that are under-capitalized will have, by default more enormous appetite for risk, to make any substantial income from trading, while those more highly capitalized might be more patient and with a lower risk appetite. Additionally, with experience, the risk appetite might decrease on those traders with weaker performance and increase with those with very consistent performance. But the overall appetitive will very likely impact traders' performance regardless because the more size one puts on, the more psychological impact there will be on trade.

A generalized approach to risk only 1% on all markets is not a very accurate way to go about it, based on all the variables presented above, each play, each market, each asset could be a little different on how much risk should trader actually put on a trade. In general, that means risk could fluctuate between 0.5% to 5%, but in general, trying to keep it as even as possible for each account is the best way to go. That way, the equity curve is more neutralized (for example, 2% risk crypto, 5% stocks, 1% FX). For beginners, it is just best to equalize the risk to fixed % per trade across all markets and then slowly add the variables explained above into each market/play to sharpen and adjust the actual risk % ratio per each play. The advantage of using consistent same % of the risk on each trade at the beginning will teach beginner trader discipline and avoid chaos. However, over time traders should figure out all the differences that come to more accurate position sizing.

Often traders completely neglect the variables listed above that should guide the % risk per trade. Instead, they make the risk % on the fly, figuring it out by what they are comfortable with. In some cases, they get away with it, but in some, they won't if any of the above variables play a very strong role in that traded asset or play. It is unwise to ignore those variables.



Dynamic versus static position sizing





What happens if everything above that has been written is taken as not necessary, and the trader wings it into position sizing, using the same position size in terms of shares or lots? What happens to the equity curve and consistency in trading?


Many traders trade with fixed position sizes on trade to trade basis, something especially widespread in FX markets where a trader will use a fixed two lot position size on each trade, and when he is asked to specify the risk, he will outline his risk in pips and not in $ or Euro of account balance.

By default, for each trader internally, his/her risk can only be defined in R units or the dollars of equity balance, never in pips or cents (distance of move). Using the distance of movement as a risk guide will create an un-consistent impact of trades on the account and big swings on account, especially with fixed position sizes.


In a low volatile market, 10 cent distance trade could be significant price distance, while in a highly volatile market that same 10 cent distance might be a very tight price distance, thus it makes very little sense to equate the risk-on trade across 100 samples in terms of a cent or pip distances. It has to be done in R units instead (by combining a specific % of equity account exposure as well).


Below are two examples of using 1000 shares (fixed static position size) on two different assets using the same type of play (conceptual setup of shorting near the market open). The short entries presented on both setups are not something that one should trade or did I trade. It is just used as a similar conceptual setup to use as an example. Two tickers CODX and FRAN, both with different levels of volatility, are shown in the image below. Both examples show how this volatility difference impacts traders who trade with fixed position sizes across all setups or assets. Those two setups are taken as an example if the trader uses chart levels such as the last high to stop out of the trade.




The above two setups show that with static position sizing, the trader is risking twice as much (and potentially being rewarded twice as much) on FRAN ticker relative to CODX. Many traders are entirely aware of the volatility they trade, but many, especially less experienced traders are unaware of it. Using the same position size on two completely different volatile assets will bring randomness into trading performance. An X position size on a low volatile asset might be a smaller loss, but the same X position size on the highly volatile asset will be a much heavier loss. By using the same size on two such different assets trader is decreasing edge, and increasing randomness into his equity curve.


Many traders without understanding the impact of volatility and correlation to position sizes will sooner or later have significant losses on heavily volatile assets such as AKTX, BPTH (2019) in small cap equities, USDTRY (2018) and USDRUB (2014) in Forex, Nanocoin (2017) in crypto etc...


Below are as well some examples from the gold (XAUUSD). As a theoretical/conceptual example, let's say that this trader is entering into a short position when the price retests the previous high (on candle close), using stop-loss above highs. The examples below show how drastically different those trades would impact the P/L and account balance as the volatility on asset changes over time IF trader uses static position sizing. The impact is so significant that just not addressing that issue alone can make the strategy fail or it can make results completely different.




The example above shows that the distance of stop loss in the 3rd setup is twice as sized as in the first setup in terms of pip/price distance units. This means that trader with using fixed / static position size if he/she enters all trades the same with 1 lot size that leaves P/L account balance impact respectively:

1. -40 EUR

2. -60 EUR

3. -80 EUR

This leads to volatility controling traders' performance, rather than trader doing it the other way around. Trades should each leave the same impact on account balance (or as similar as possible) unless the trader decides to intentionally size more on a specific setup because the setup has higher grade variables present and trader has higher conviction on a trade. That is, however, a completely different story for discussion.

Using gold as traded asset again below, if the trader was to use the same "strategy" as shown on image above and static position sizing, placing the stop loss above high, the stop loss distance would be around 20 pips.




If a trader were stopped out of this trade, that would leave such account P/L with one lot:

-200 EUR


Overall the trades' impact on the example of gold to such traders account balance would vary from -40 EUR to -200 EUR, creating huge swings even though all the setups taken were technically the same type of setup. Ideally, traders equity impact on each trader should be somewhat similar if the same type of setup is traded and not varying at such wide-scale as 40-200 EUR difference. And again, let's point out, such swings are normal in any trading account. Still, it is entirely different if those swings result from traders intentionally risking more on specific setups or risking less on other setups due to A or B grade variables. The above gold example is used where the trader is treating each trade as neutrally adjusted to the rest of the trades, and that is where static position sizing could damage trading strategies performance. One can have the edge at reading markets but if the trader does not understand the concept of proper position sizing and adjusting it to volatility, it can be enough to bury the trading performance in randomness and swings that make little to no sense.


Below are my examples of how I quickly determine the position sizes on FX and equities (excluding the variables explained above that weight additionally to size of position adjustment):


Forex (using the simple indicator to input SL distance in pips and to determine lot size):




Equities (using simple distance measure tool in TC2000 to measure cents and then multiplying by the required number of shares to achieve % acc risk):




Some traders just use a spreadsheet, which is also a quick way to determine the position size, but if you are a visual trader always using highs or lows or some chart area to set stop-loss, then the above methods will suit you better to calculate position sizes quicker by using quick draw/measuring tools to first get the info on the actual price distance in units.

The general rule is to use less shares/lots on highly volatile assets and more shares/lots on less volatile investments. But keep in mind that less volatile assets will cost you more in commissions due to more shares or lots being used usually unless all orders are filled without removing liquidity on the limit (adding to liquidity instead of taking it).




Before entering an asset trader should alway measure the volatility, using the simple measurement tool in any trading software. A must before any planning of entry. If this step is not done, then quickly everything else can cascade with other bad decisions if the trader gets all of sudden large red P/L without realizing what is going on.

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