Better System Trader @bettersystrader

Most momentum traders treat their signals as binary. Momentum is either present or it isn't. Alex Spiroglou's research identifies seven distinct ranges within the momentum cycle, each with different behavioral characteristics and different optimal trading responses. Most traders only operate in one or two of those ranges. They take signals at points in the cycle where follow-through probability is actually quite low, and they exit at points where the move typically has the most remaining distance. The seven-range model isn't about being more selective. It's about understanding where in the cycle a signal fires and what that implies about likely behavior from that point forward. A momentum reading near the extreme of its historical range behaves differently than the same reading in the middle of the range. The value tells you the direction. The range tells you what stage of the momentum lifecycle you're actually in. The traders who manage momentum positions well aren't better at reading signals. They're better at knowing what each stage of the cycle actually means.

Full episode with Jason Shapiro: youtube.com/watch?v=gynU0U… Episode notes: bettersystemtrader.com/203-going-agai…

Most contrarian traders act on extreme COT positioning. Jason Shapiro waits for something else. The setup: the crowd is extremely positioned in one direction. News comes out that should push the market that way. The market doesn't move. That's the entry. Not the positioning. If a market can't rally on bullish news, the crowd that bet on it is trapped. They're positioned for a move that isn't coming. Eventually they'll have to exit, and their exit provides the actual move. Jason has traded this approach across 35 US futures markets for 30 years. Every position is sized to 75 basis points of defined risk. The entry isn't "this market looks cheap." The entry is "this market just refused to do what the crowd needed it to do." Most traders who use COT data stop at the positioning. Jason says the positioning just tells you who might be trapped. The news failure tells you they are.

Full episode with Richard Metzger: youtube.com/watch?v=BoKMfj… Episode notes: bettersystemtrader.com/191-combining-…

Richard Metzger spent 30 years as a logic design engineer before becoming a systematic trader. His approach to algorithm construction reflects that background: every component has a defined state, and behavior is governed by which state is active. His State-Based Market Design framework works like this. 1. Define three market states: up, down, and sideways... States are determined by objective monthly thresholds applied to the S&P 500. Not discretionary reads. Measurable criteria that trigger a state transition. 2. Build a "hero algorithm" for each state... An algorithm optimized specifically to perform in up markets, one for down markets, one for sideways conditions. Each algorithm will be a "villain" (performing poorly) in the other two states. That's fine. You're not trying to build an all-weather strategy. You're building the best possible strategy for each specific environment. 3. Combine them into a portfolio where each algorithm's weight is proportional to how frequently its corresponding state occurs... If up markets represent 60% of time, down 20%, and sideways 20%, the weights follow those frequencies. The result: a portfolio that's always running the algorithm designed for the current environment, not a compromise strategy that performs adequately in all three but excellently in none. The execution challenge is state transitions. When the market moves from one state to another, the portfolio rebalances between algorithms. Richard's framework includes rules for handling this without over-reacting to short-term noise that could trigger false state changes. Most systematic traders build one strategy and try to make it robust to all conditions. State-Based Design inverts this: accept that different conditions require different approaches, then build the best possible approach for each. Bookmark this. Lots to work through here.

Full episode with Richard Brennan: youtube.com/watch?v=ZaF6Ev… Episode notes: bettersystemtrader.com/surviving-unce…

Most traders diversify to improve returns. Richard Brennan says that's only half the picture, and probably the less important half. His view: diversification across 50+ markets is primarily a risk management tool. The alpha justification comes second. The logic: if you accept that fat-tail moves are more common than standard probability models predict, then your ability to capture them depends on being in enough markets that you're likely to be positioned when one occurs. You can't predict which market will produce the next 100R move. You can make sure you're in most of the markets that might. This reframes the diversification question... Most traders ask "how many markets do I need to improve my Sharpe ratio?" Richard's question is "how many markets do I need to be likely to catch the tail events that drive most of the long-term returns in trend following?" The answer to the second question is closer to 50 than to 5 or 10. If your diversification is designed to smooth returns rather than maximize exposure to fat-tail events, you might be optimizing for the wrong thing entirely.

Full episode with Kevin Davey: youtube.com/watch?v=nMZJOT… Episode notes: bettersystemtrader.com/187-how-to-spo…

Many traders think spotting a trading scam is about analyzing the numbers. Kevin Davey says the lifestyle marketing is a more reliable tell than anything in the performance claims. Backtested equity curves can be manipulated. Performance claims can be fabricated. But the marketing style reveals something the numbers can't be faked to hide: who the seller thinks they're actually selling to. Genuine trading services don't need to lead with private jets, expensive cars, or screenshots of massive withdrawals. Traders who actually produce the results they claim tend to market on the research and the process, not the lifestyle outcome. The lifestyle pitch is designed to bypass your analysis and activate a different response. It's not trying to convince you the strategy works. It's trying to make you feel like you're missing out on the life. Kevin's rule of thumb: if the marketing makes you feel excited rather than curious about the methodology, treat that as a red flag. Real edges get explained, because the explanation is what attracts serious traders in the first place.

Nick Radge has been trading trend following systems since 1985. When I asked how he identifies a trend, he gave me eight different answers. All of them work. The eight approaches range from simple to slightly less simple: - Price channels. - Rate of change filters. - Bollinger Band breakouts. - Relative strength rankings. - Moving average crossovers. - High-momentum breakouts. - The 20% Flipper - buying stocks that have risen 20% from their recent low. - A few others. What's interesting is that he doesn't advocate for one above the others. His view is that simplicity itself is the edge, and many different simple implementations of the same basic idea will produce similar long-run results. "Simple is more robust than complex. Complex systems have more ways to break." This isn't what most traders want to hear. There's a persistent belief that successful traders have found some sophisticated, proprietary edge that the rest of the market hasn't discovered yet. After 30 years of systematic trading, Nick's view is roughly the opposite. The edge in trend following isn't a secret formula. It's the discipline to apply simple rules consistently over long periods, through drawdowns that make most traders abandon the strategy right before it recovers. The complexity most traders are searching for doesn't add edge. It adds fragility.

Full episode with Nick Radge: youtube.com/watch?v=byqEyu… Episode notes: bettersystemtrader.com/004-nick-radge/

Full episode with Brent Penfold: youtube.com/watch?v=ovCdb6… Episode notes: bettersystemtrader.com/using-big-pict…

Most support and resistance tools require you to set parameters. Brent Penfold's Market Ladder doesn't. The problem with parameterized S/R tools: the levels you get depend entirely on the settings you choose. Change the settings, change the levels. Traders end up tuning the tool to fit the history rather than discovering where the market actually respects price. The Market Ladder is built on historical pivot highs and lows identified from price structure alone. No inputs, no optimization. The levels either appear in the history or they don't. The result: the levels you're working with reflect where the market has actually reversed, not where a formula says it should have. Combined with Brent's fractal framework, you can layer current timeframe pivots over higher timeframe history and immediately see which of your current levels have deeper historical significance. Most traders draw levels that feel right. This approach finds levels the price data actually supports.

There's a 20-trading-day cycle present in most stocks. John Ehlers has been using it to time entries for decades. Many traders have never heard of it. John has spent decades applying digital signal processing theory to market analysis. One of his consistent findings: most stocks contain a dominant cycle of roughly 20 trading days. This isn't vague market mysticism. It's a measurable, repeating pattern in price data that shows up across broad equity markets with enough regularity to be useful. The 20-day cycle means stocks tend to move from local lows to local highs and back over roughly one calendar month. The amplitude varies. The timing isn't precise. But the pattern is consistent enough to inform strategy design. Most retail mean reversion systems use arbitrary lookback periods: 2-period RSI, 5-day low, 10-day range. John argues those numbers should be informed by the actual cycle present in the data, not picked from convention or backtested into the best-looking result. His indicator is parameterized to align with cycle-based turning points rather than arbitrary time windows. Traders who ignore cycle analysis are timing mean reversion entries without knowing whether they're entering at the right point in the cycle or the wrong one.

Full episode with John Ehlers: youtube.com/watch?v=tk49Qh… Episode notes: bettersystemtrader.com/103-a-new-resp…

Most traders who don't trade options assume options markets are someone else's problem. Fabio Ruggeri's data suggests that's been wrong for the past five years. Pre-2020, options volume was dominated by institutional hedging. The effect on price action was real but manageable. After 2020, retail options participation exploded. The flows got large enough that market maker hedging became a primary driver of short-term price behavior in indices like SPX and in individual large-cap stocks. When market makers are net short gamma, they buy as price rises and sell as it falls, which amplifies moves. When they're net long gamma, they do the opposite, suppressing moves and compressing volatility. This regime switch happens at known price levels, and the data to identify them is publicly available. Most price-based systems were built before this dynamic was this influential. The traders ignoring options flow are operating with a model of how price behaves that the market partly left behind after 2020.

A standard RSI can get you into a trade 7 bars into a 10-bar move. John Ehlers proved this mathematically. John applies digital signal processing theory to trading indicators. His finding about lag is worse than most traders realize. Traditional indicators like RSI and moving averages compute using a window of past prices. By definition they're always looking backwards. By the time the signal fires, the move that generated it is already significantly underway. John measured this specifically. In a typical 10-bar cycle move, a standard RSI fires a signal around bar 7. You're entering with 3 bars left. In many setups, that's a guaranteed loser. His solution uses a reverse EMA approach from digital signal processing. A standard EMA is computed left to right, each bar weighting the previous. He computes it right to left across a finite window, then subtracts the result from the forward EMA. The subtraction cancels the distortion. The result is an oscillator that responds to market turning points significantly faster than any traditional indicator, without the noise problems that plague other "fast" approaches. Most traders try to solve lag by choosing faster indicator settings. John solves it by changing how the calculation is structured.