Most traders building automated systems never look beyond their price charts. Tomas Nesnidal spent six months doing what almost no one else has done: building a complete library of market internals conditions from scratch, testing 32 different filters against his existing strategies, and finding results that, in his words, felt genuinely unfair. Not to him. To everyone trading without them.
Tomas Nesnidal is a Czech trader and system developer who specializes in automated breakout strategies. He first appeared on the Better System Trader podcast in episode 43, where he shared a breakout strategy toolkit that became one of the most downloaded resources on the show. He chose automated trading so he could travel freely with his wife without being tied to screens, and that discipline forced him to build systems that could work without his constant attention. In episode 52, Tomas explains what market internals are, why they are so rarely used well, and how they can turn a losing system into a profitable one.
Watch the full episode below, then read on for the complete breakdown.
Why Tomas calls market internals an unfair advantage
The phrase unfair advantage is not marketing language. Tomas uses it because he found the information available on market internals to be almost non-existent or actively misleading. Online guidance often recommends using a particular market internals indicator in one direction. When he tested the opposite direction, it worked better. The conventional wisdom was wrong.
The real edge is not just in knowing what market internals are, but in building your own research. Most traders who have heard of these tools either ignore them or apply them superficially. The traders who go deep and develop their own conditions are operating with information that the majority simply do not have. That gap is what makes it unfair.
“Very few traders know about it. And it’s something that doesn’t have the risk of making your strategy too complex or over-optimized. It’s quite robust and quite reliable.”
What market internals actually are
Market internals are data feeds provided directly by stock exchanges that summarize the behavior of all stocks being traded on the exchange at any given moment. Rather than looking at a single instrument like the S&P 500 index, market internals show you what the entire exchange is doing.
The main data types fall into three categories: price-based, volume-based, and volatility-based. Common data feeds include TIC and TICI (tick-level data), U-Wall and D-Wall (up volume and down volume pressure on the market as a whole), and VIX (market volatility). Most traders are familiar with VIX, but far fewer work with U-Wall, D-Wall, or TIC conditions in their systems.
The key insight is that market internals provide a broader view than any individual instrument can. If you are about to take a long signal on a specific stock or futures contract, knowing whether the overall exchange is under heavy selling pressure changes the quality of that signal.
The superfilter concept
Tomas frames market internals as superfilters. A superfilter sits on top of an existing strategy and uses the broad market picture to decide whether to take a signal or stay out. It does not replace the strategy logic. It acts as a final checkpoint before entry.
The application works in both directions. On the entry side, a superfilter might block a long trade when overall exchange volume is running heavily to the downside. On the exit side, it can trigger an early exit or a tighter stop when broad market conditions shift against an open position. In both cases, the decision is driven by exchange-wide data rather than the individual instrument chart.
The advantage over other filters is simplicity. You are not adding complex rules to the core strategy. You are adding a single layer of context that either allows or blocks what the strategy already wants to do.
Six months of research, 32 conditions
Tomas did not find a ready-made framework for using market internals in automated trading. He built his own from nothing. After realizing that publicly available information was either too vague or contradictory, he committed to six months of daily research, testing every variation he could think of.
The outcome was a library of 32 market internals conditions. These range from basic comparisons of up volume versus down volume, to ratio-based measures, to applications of Bollinger Bands applied to market internals feeds. He also applied statistical arbitrage concepts to market internals, though he found that the simpler conditions often worked better than the more complex ones.
Not every condition works on every strategy. The filters are applied one by one to existing systems, and which one produces improvement depends on the underlying logic of the strategy. Some strategies show no improvement at all. Others show dramatic results.
What kind of results are possible
The results Tomas describes are the reason he calls this an unfair advantage. Applying market internals conditions to failing strategies has, in some cases, turned them profitable. More commonly, working strategies see significant improvement in specific metrics.
The specific results he documents include drawdown reductions of up to 50%, improvements in the net profit to maximum drawdown ratio of up to 100%, increases in profit factor, and higher average trade values. Not all of these appear together on every strategy, and results vary by system and by which conditions are applied. But the consistency of meaningful improvement across multiple strategies and multiple traders is what convinced him these filters were worth building into every system he trades.
He also notes that because market internals act as a filter rather than a modification of the underlying strategy logic, they carry a lower risk of overfitting. You are not curve-fitting the strategy to historical data. You are adding context that reduces trades taken in poor broad market conditions.
The right way to use public online guidance
One of the sharper observations in the episode involves what happens when traders follow commonly available guidance on market internals. Tomas found that the standard advice on how to use TIC, for instance, often pointed in the wrong direction. When he tested the recommended approach, results were mediocre. When he reversed the logic and tested the opposite, results improved.
His conclusion is not that the publicly available information is deliberately wrong. It is incomplete. General guidance describes broad tendencies but does not capture the specific conditions that actually improve performance in automated systems. The only way to find what works is to test it yourself, systematically, across your own strategy library.
Market internals in automated versus discretionary trading
Tomas discovered market internals first as a discretionary day trader, where they gave him a modest but real edge. When he transitioned to automated systems, he forgot about them entirely. Years later, when some of his strategies began struggling, he went back to the concept and tested it thoroughly in a systematic context.
His finding was that market internals are actually more powerful in automated trading than in discretionary trading. In discretionary trading, you can intuitively adjust to what you see in the market. In automated systems, the code is fixed. Adding a well-tested market internals filter provides that adaptive layer that a purely mechanical system lacks.
Related episodes
- Innovation as a Trading Edge – Dr Brett Steenbarger
- How to Avoid Curve Fitting Using Independent Testing – Jeff Swanson
- How to Get Unlimited Trading Ideas – Rob Hanna (Episode 7)
- How to Leverage Market Biases – Lawrence Chan (Episode 204)
Get the show notes & transcript
Want to learn how to add market internals to your trading systems? Subscribe to the Better System Trader podcast for weekly interviews with the world’s top systematic traders and quantitative researchers.
