The Automated Trading System Of A 3 Time Trading World Champion Kevin Davey

TITLE: The Automated Trading System of a 3-Time Trading World Champion | Kevin Davey CHANNEL: Financial Wisdom DATE: 2026-06-08 ---TRANSCRIPT--- Hi all, today we discuss the book building winning algorithmic trading systems by professional trader Kevin J. Davyy. Kevin Davey achieved something remarkable. He won or placed second in the World Cup championship of futures trading for three consecutive years with 148% 107% and 112% returns. He achieved those returns using a systematic algorithmic approach that turned him from a losing trader into a champion. The reason Kevin’s framework for building trading algorithms caught my attention is that I had unknowingly followed a very similar process myself while developing and refining my rules-based swing trading strategy. Over the years, the strategy has produced this equity curve and these performance stats. Looking back, I believe that if I had discovered Kevin’s framework earlier, the entire process of building the strategy would have been far quicker and much smoother. Still, regardless of how long it took, once the system proved itself through results, I went allin. I even developed a bespoke scanner specifically designed to execute my lateral breakout strategy efficiently. Today, it allows me to manage the entire process with just a few minutes of work over the weekend. If you’d like to learn more about the scanner, download the free strategy ebook, or understand how I personally trade, check out the links in the description below. Kevin Davy’s story will sound familiar to many of you. From a 60% account loss using moving averages to a $70,000 loss averaging down, he had multiple account blowups. But he persevered in his trading journey and his transformation from chronic loser to world cup champion wasn’t luck. It was pure grit to learn trading and make a living out of it. He gave up a cushy aerospace job for this passion in 2008 and has never looked back since. Let’s understand what makes a successful algorithmic trading strategy. According to Kevin, a successful algorithmic strategy typically follows a strategy factory process which includes seven critical phases. Step one, smart goals for his euro system, a mean reverting system, which we’ll discuss later in the video. His goal was to create a trading system that earns 50% annual return with a maximum 25% draw down, wins 55% of trading days, and takes a maximum of two trades daily. Notice the specificity in setting goals. Every number is measurable. Step two, the trading idea. You need a logical basis for a strategy such as a mean reversion approach that enters a market when it has stretched too far from its average price or a breakout approach like mine that bets on the continuation of trends. The primary goal here is to identify a statistical edge, a logical reason why a strategy should make money. For example, a statistical edge for a strategy could be being right half the time and making three times the reward to risk in an average winning trade. Step three, feasibility testing. Before risking significant time or money, Davey tests on tiny data samples, just 1 to two years. He breaks down testing into three parts. Entry testing, exit testing, and the core trading system. In entry testing, the goal is to determine if an entry signal has any usefulness or a statistical edge. For example, to enter at the close of the breakout bar or at the cross above the level is a question that will be answered at this stage. In exit testing, the goal is to test exit efficacy. Davey notes that exits have a huge impact on profitability and can sometimes make even a mediocre entry signal profitable. This again can help answer the question whether to close the position at the close of a bar or just at the cross of a level or at a specific time in market hours. In core system testing, Davey evaluates the interaction between the entries and exits on a small chunk of data, usually 1 to two years, looking for broad profitability and a logical basis. However, here is an uncomfortable truth. A strategy that passes with flying colors in the back test might fail miserably in real world scenarios due to a number of reasons, including slippages and commissions. Davey calls it the optimization trap. That’s why Davey insists on step four, walk forward analysis, the reality check. Instead of optimizing on all your data, you optimize on a small portion, say a fifth or third of the data, and then test on unseen data with real world constraints like liquidity, slippages, and commissions. Repeat this process rolling forward through time. The results are often sobering, but they’re real. Step five is stress testing with Monte Carlo. Davey uses a Monte Carlo simulation to find out the worst case scenario. Monte Carlo simulation is a statistical process that shuffles the order of past trade results thousands of times to create a family of possible equity curves. It gives you the chance of ruin, your max draw down, expected return over time and the probability of profit in a given period. All these metrics are essential for the final go ahead on any strategy. Step six is incubation, a mandatory watch and wait period of 3 to 6 months where the strategy is run on live data without real money to ensure it matches back tested results. Step seven is diversification, position sizing, and live operation. It involves diversifying across multiple uncorrelated systems to create a smoother equity curve and reduce overall volatility. By combining strategies with different markets, time frames or logic such as long short systems, the failure or draw down of one strategy is cushioned by the performance of others. Following this, position sizing determines the specific number of contracts to trade as account equity grows. Finally, the strategy transitions into a full-size real money operation. This phase demands absolute discipline to execute every signal exactly as programmed as any manual interference or cherrypicking invalidates your statistical edge. A vital prerequisite is establishing a definitive quitting point. A specific draw down level where you agree to stop trading to protect your capital from a broken system. Once live, you must continuously monitor performance. If the actual equity curve falls significantly below historical expectations, you must have the humility to stop trading. Let’s examine Davey’s euro system which perfectly illustrates his principles in action. He created two complimentary strategies, Euron night strategy and Euro day strategy. Step one, smart goals. Davey established a single set of smart goals for the euro system. Performance 50% annual return with a median maximum draw down of 25% or less. A return to draw down ratio of more than or equal to two. Consistency win on 55% or more of trading days. Operational no more than two trades per day. And time bound developments to be completed within one month by the end of March 2013. Step two, trading idea, the edge. Davey identified that different market personalities exist at different times of the day, leading him to create two distinct strategies for eurourrency futures. The Euro night strategy designed for the overnight session 6 p.m. to 7:00 a.m. Eastern time using 105minute bars. It used a reversal entry, long entry based on the average high of previous bars reduced by an ATR multiplier. That means the strategy enters a long trade if the price goes too far below the average high of a set number of previous bars, aiming to profit from a reversal from the overextended decline. Its goal is high-winning percentages with frequent small wins. and the Euro day strategy designed for the liquid US session from 7:00 a.m. to 3 p.m. Eastern time using 60-minute bars. It uses mean reversion logic. It focuses on trading in the direction of the main trend by entering the extremes of counter trends. So if the main trend is up, it will wait for the price to dip to extremes in counter downtrends to buy to participate in the main uptrend. This is the primary profit generator where profits are allowed to run for a little longer. Step three, limited feasibility testing. Davey tested both ideas on a small chunk of data, the year 2009, to verify their merit. Euro night, 82% of entry optimizations and 85% of core system optimizations were profitable. Euro day 76% of entry optimizations and 81% of core system optimizations were profitable. Monkey testing both entries and exits proved significantly better than random dart throwing signals. Step four, walk forward testing and optimization. Both strategies underwent rigorous walk forward analysis from July 2009 to March 2013 to ensure they were not curve fit to the past. The analysis stitched together out of sample periods to create a realistic historical equity curve for both strategies which Davey deemed successful enough to proceed. Step five, Monte Carlo simulation. Davey ran 2500 iterations to determine the expected performance and risk of ruin for each strategy. Euro day expected 129% return with a 23.7% median draw down a return to draw down ratio of 5.45 and Euronite expected 52% return with a 25% median draw down a return to draw down ratio of 2.0. combined system. By analyzing both together, the return to draw down ratio jumped to 6.6 with an expected 176% return, proving that diversification made the combined system stronger than its parts. Step six, incubation. Davey monitored the strategies on live data without real money for 5 months, March 2013 to August 2013. The system passed this stage statistically and visually, confirming its potency. Step seven, diversification, position sizing, and live operation. Davey transitions to live trading on August 20th, 2013 with the following parameters: account funding started with $8,500 to balance risk of ruin and contract growth. Position sizing used fixed fractional sizing of 17.5% determined via Monte Carlo to maximize returns while keeping ruin risk below 10%. Quitting point he established a predefined stop trading level of $5,000 drawdown per contract to protect his capital monitoring. He tracked live performance by week 15. and the actual performance was showing a 9.0% profit which was outperforming the strategy calculated expectctions. Here is the psychology problem with algorithmic trading. Systematic trading isn’t emotionless. The emotions just show up differently. Instead of panicking during trades, you’ll be tempted to skip signals after losing streaks. Instead of revenge trading, you’ll want to improve your system midstream. Davey’s solution is brutally simple. Write down your rules before emotions interfere. For his euro system, the quick condition was non-negotiable. Stop trading if the draw down exceeds $5,000 per contract. Period. No exceptions. No, just one more trade. Finally, building algorithmic systems requires you to code your conditions on a terminal. For that, Kevin’s advice is to learn to do it yourself, to not give away your secret source to a hired hand. There are plenty of free and paid resources available that can get you started in no time. Algorithmic trading can be stressful at first when you’re figuring out the technicalities, but are extremely liberating once you build a system that doesn’t lose relevance with time. I built one such system which may not be completely algorithmic but is bound by rules at each step that makes my trading seamless, emotionless, stress-free and most importantly rewarding. You can also build one for yourself. As always, thanks for watching and for more on my personal strategy, you can watch this video.