Calculating lot size for a Forex order based on portfolio value and leverage

I am currently trying to understand the calculation of Forex lot sizes based on portfolio value and leverage.

In the following, highly simplified example

from clr import AddReference AddReference("System") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Indicators") AddReference("QuantConnect.Common") from System import * from QuantConnect import * from QuantConnect.Algorithm import * ### <summary> ### lot size calculation ### </summary>> class LotSizeCalculation(QCAlgorithm): def Initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.SetStartDate(2015, 12, 1) #Set Start Date self.SetEndDate(2015, 12, 1) #Set End Date self.SetCash(5000) #Set Strategy Cash self.AddForex("EURUSD", Resolution.Minute, Market.Oanda) self.SetBrokerageModel(BrokerageName.OandaBrokerage); def OnData(self,data): portfolioValue = self.Portfolio.TotalHoldingsValue orderSize = #? self.MarketOrder("EURUSD",orderSize)

How would I calculate orderSize such that it is the maximum possible value based on the portfolio value and maximum leverage?

Hi Filib,

You can create a ratio to determine what portion of your margin you should use to limit your max loss to 1% for an entry at that price.

Max Loss for trade = ((pip * 30)/price) * orderSize
Max Loss for Portfolio = 0.01 * margin 

# We can set those equal to restrict our Max Loss for a trade to be at most 1% of our portfolio margin.
((pip * 30)/price) * orderSize =  0.01 * margin 

# This implies that
orderSize = (0.01 * margin * price) / (30 * pip)

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Hey Filib,

First we need to know how much leverage we have at our disposal. One way to access the available leverage is through the Security object for the symbol we want to trade, leverage = self.Securities["EURUSD"].Leverage. The default leverage for Forex is 50.

Next, we want to know the margin we have available to use, this can be accessed through margin = self.Portfolio.MarginRemaining.The total buying power we have in USD is margin * leverage.

To find how many EUR we can purchase, let's divide by the current exchange rate.

leverage = self.Securities["EURUSD"].Leverage margin = self.Portfolio.MarginRemaining orderSize = margin * leverage / self.Securities["EURUSD"].Close

You can also use self.SetHoldings(symbol, 50) to enter the largest possible position,
or if you want to calculate the maximum quantity, you can use self.CalculateOrderQuantity(symbol, 50)

Note: Being fully levered makes you highly susceptible to margin calls.

def trail_stop(self):
for symbol, sd in self.symbol_data.items():
if not sd.IsReady:
continue
mean_price = np.mean(list(sd.close_data))
# Stop loss percentage is the return over the lookback period
stoploss = abs(sd.weight * self.lookback / 252.0) + 1 # percent change per period
if sd.weight > 0 and sd.stopprice is not None:
if sd.stopprice is not None and sd.stopprice < 0:
sd.stopprice = mean_price / stoploss
else:
sd.stopprice = max(mean_price / stoploss, sd.stopprice)
if mean_price < sd.stopprice:
sd.weight = 0
self.Liquidate(symbol) elif sd.weight < 0 and sd.stopprice is not None:
if sd.stopprice is not None and sd.stopprice < 0:
sd.stopprice = mean_price * stoploss
else:
sd.stopprice = min(mean_price * stoploss, sd.stopprice)
if mean_price > sd.stopprice:
sd.weight = 0
self.Liquidate(symbol) else:
sd.stopprice = None

+ Expand

Rahul Chowdhury INVESTOR

Rahul Research | February 2020

Upvote

Filib Uster INVESTOR

February 2020

Thanks a lot for your answer, Rahul Chowdhury !

Since you mentioned the risk of margin calls, let's add a risk factor and stopLoss/profitTargets to the calculation.

Assuming that I don't want to risk more than 1% per trade and my strategy sets a stop loss of 30 pips below and a profit target of 20 pips above the current market price:

price = self.Securities["EURUSD"].Close

pip = self.Securities["EURUSD"].SymbolProperties.MinimumPriceVariation

leverage = self.Securities["EURUSD"].Leverage

margin = self.Portfolio.MarginRemaining

risk = 0.01

stopLoss = price - pip * 30

profitTarget = price + pip * 20

orderSize = ?

How would I calculate the orderSize in this case?

Sorry, could not edit anymore, but obviously, in the previous post I should have said

Assuming that I don't want to risk more than 1% of my portfolio value per trade

Still struggling with some of the constraints of the Forum software, keeping me from deleting or editing previous posts. Lets try again:

Assuming that

I don't want to risk more than 1% of my portfolio value per trade
my strategy only enters one position at any time
my strategy sets a stop loss of 30 pips below and a profit target of 20 pips above the current market price per trade

price = self.Securities["EURUSD"].Close
pip = self.Securities["EURUSD"].SymbolProperties.MinimumPriceVariation
leverage = self.Securities["EURUSD"].Leverage
margin = self.Portfolio.MarginRemaining
risk = 0.01
stopLoss = price - pip * 30
profitTarget = price + pip * 20
orderSize = ?
MarketOrder("EURUSD", orderSize);
StopLimitOrder("EURUSD", -orderSize, profitTarget, stopLoss);

How would I calculate orderSize in this case?

Thanks a lot! (pun intended....)

1 person upvoted this

Shile Wen INVESTOR

UW CFRM | January 2021

Hi Zach,

The algorithm has many technical and stylistic issues, and since it seemed to be a popular algorithm, we've rewrote it to use the SymbolData pattern, replaced the History calls with RollingWindows, and fixed many stylistic issues. Please view the updated algorithm in the attached backtest.

Best,
Shile Wen

3 people upvoted this

Zach Oakes INVESTOR

January 2021

Thanks ! It's very cool. It's like an MR take on Trend -- brilliant interpretation, and MUCH nicer than my translation.

Mohamed Ajmal INVESTOR

June 2021

How can we implement stoploss / reduce drawdown in this algorithm?

Varad Kabade INVESTOR

July 2021

Hi Mohamed Ajmal,
In the last backtest attached by Shile he has implemented the stop-loss which is triggered every day after 10 minutes of market open:

 self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 10), self.trail_stop)

Refer to the following code snippet.

def trail_stop(self):
        for symbol, sd in self.symbol_data.items():
            if not sd.IsReady:
                continue
            mean_price = np.mean(list(sd.close_data))
            # Stop loss percentage is the return over the lookback period
            stoploss = abs(sd.weight * self.lookback / 252.0) + 1    # percent change per period
            if sd.weight > 0 and sd.stopprice is not None:
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price / stoploss
                else:
                    sd.stopprice = max(mean_price / stoploss, sd.stopprice)
                    if mean_price < sd.stopprice:
                        sd.weight = 0
                        self.Liquidate(symbol)            elif sd.weight < 0 and sd.stopprice is not None: 
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price * stoploss
                else:
                    sd.stopprice = min(mean_price * stoploss, sd.stopprice)
                    if mean_price > sd.stopprice:
                       sd.weight = 0
                       self.Liquidate(symbol)            else:
                sd.stopprice = None
+ Expand
- Collapse

Best,
Varad Kabade

Andres Arizpe INVESTOR

January 2022

This appears to be a very interesting algo.

Is there any documentation I might use to get a basic understanding of it?

Cheers,

Andres

Hi Andres Arizpe,
The above algorithm consists of components like the scheduled events, rolling window, and standard python libraries like numpy and pandas. We recommend going through the following docs[1, 2], and regarding the libraries, please look for their homepage/documentation. Please feel free to ask any specific doubts about the above algorithm.
Best,
Varad Kabade

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