Hi Everyone, In building out my first trading BOT, I have struggled around determining what is an acceptable and realistic profit factor and profitability % that one could expect to use a benchmark to strive toward for improvement. Not trying to aim for pie in the sky numbers, just keeping it real. I often see folks talking about striving for a 1.5 Reward: 1 Risk or for a 2 Reward:1 Risk ratio. Is this the benchmark I need to stay above? is a 3:1 or 4:1 reasonable or just not realistic to maintain? In terms of profitability, can a 40% profitable system still win over time? What is the minimum amount of % profitability do I need to stay above so that it can grow an account? Is there a chart/table that exists that may illustrate this better? My risk management in the BOT is cutting the position if it falls below a 3.5% fixed stop loss, but I am considering to change this to each equity's ATR * 1.5 (maybe advisable to increase to 2 ATR with a not to exceed 8%?) Thanks for any insights and thoughts to my questions, MAC
Remember, everybody and their grandmother has a profitable backtest. If you try 20 things one of them will appear significant at the 5% level just by chance. If you think you have something, trade if for real with a little bit of money. The market will teach you in short order things that a lifetime of computer simulations missed.
Thanks for your response. I agree with you. I am running the BOT against daily market data in a paper trading account, using the same buy and sell management rules that would be followed in a Live trading account. The BOT is pulling stock price for all positions roughly every 20 seconds to monitor for a profit target price or sell stop price. This didn't get a lot of time to play in a bullish environment. First trade was on 7/10 from the daily but since 8/2, the US market has been trending down, leading to a trip of multiple stop losses for trades entered before that turn. The BOT is a bullish one, so it doesn't go short due to margin requirements. I am working on trying to reduce the position sizing based on market exposure when the overall market may not be in a bullish state. Right now, as a result of this, the BOT has a -0.56R across 53 trades. I have to get that fixed.
This is a helpful tool as it validates what I have already built into my system as I incorporated Van Tharp's system evaluation for the BOT that uses closed and currently open positions to determine System Odds, System Expectancy, System R, and System Trading Quality that provides a Ratio of Expectancy to Standard Deviation of R - my system is currently rated "Very Hard to Trade" at the moment. I plugged in my inputs as I have the BOT calculate Expectancy ($) per Trade (10,25,50,100,250,500,1000). Since this shows calculator shows per 1000 trades, I had a direct match and confirms that I have the calculations correct, but this calculator makes it easy for me to work with inputs for any simulations or strategy tests. In case someone is interested in this type of information, in the book "Super Trader" by Van K. Tharp on pages 200 - 203 cover System Quality Numbers and the Results of Unaudited Holy Grail System.
These scenarios are a bit of fun to play around with. When obtaining a BOT, recognise that even a rather mediocre manual strategy will double your money every year. The vital statistics need be no better than - win rate 55% risk per trade 1.0% gain per trade 1.5% trades per month 20 I can almost guarantee that nobody is using Youtube or Tiktok to even try to demand money for such a strategy.
In my opinion, it is not really an extraordinary calculator because it does not take into account the compound effect. I don't know how you set the stake size for each trade, but if you read Van Tharp, you probably chose to risk a certain percentage of your equity on each trade, the anti-martingale system is called by Van Tharp in his books. So when the account grows, then the stake size will grow proportionally, as a consequence, the trade expectancy will also be higher.
Correct. I use Tharp's Percent Risk Position Sizing Model. Using a 1% Portfolio Risk, I alternate between either a Fixed Stop Loss % or ATR Stop Loss to understand the 1R Risk (stop loss) and R Target (& Price Target) to know the RRR, Position Size, and actual # of Shares to Buy. It is also helpful in improving the system is a view of the Multiple R not only from an expectancy perspective, but in trying to hold to a -1R and not let those slip from a risk management viewpoint. There are many different position models that Van Tharp describes, but I have picked his Percent Risk Position Sizing Model that seems to work well for me. As a comparison, I also calculate a Kelly Criterion to see a different perspective, but the system doesn't currently use it for position sizing.
Imo, you're just looking for a positive mean return after friction with as tight stops as will allow, something that is simple so it can be executed instantaneously and act quickly on a signal, and something with as many signals as possible under those constraints. Let the rest of the stats just fall into place. You're first idea will not be your best.