Quantitative Laboratory
How to Become a Better Trader: Why Market Conditions Matter More Than Your Entry Signal
I tested roughly 85,000 Nasdaq 100 trades. The broader market environment had more than five times the impact of the entry signal. What that means for trade selection, position sizing, and risk.
One of the most popular pages on my website has been the SPY Forecast page. Over the years I've heard from plenty of traders who check it regularly and use it in different ways, most often watching for the forecast gauges to reach extremes before deciding whether to put more capital to work or become more defensive.
That page was always a simplified public version of something I have used in my own trading for years. I have long relied on a broader market model to help decide how much total risk I want to carry, not just whether I think SPY is more likely to move higher or lower.
The new forecast brings more of that process into view. Along with a Forward Map showing the expected path of SPY over the next several sessions, it now includes an exposure reading designed to answer a more practical question: how much risk is the current market environment actually supporting?
Before getting into the new forecast, I wanted to show why that distinction matters.
Market Conditions vs. Entry Signals: What Actually Drives Trade Results
Most traders spend a lot of time on the entry: which indicator to use, where to set the threshold, whether to wait for confirmation. It feels like the part that matters because it is the part you control.
I ran roughly 85,000 trades across Nasdaq 100 stocks going back to 2003. The setup and five-day holding period stayed the same. What moved the results most was not the entry. It was what the broader market was doing when the signal fired.
In one market environment, the trade lost money about four times out of ten. In another, it made money about six times out of ten. Nothing about the setup changed. Only the market around it did.
I have been building systems around that idea for years. Here is the data behind it.
The Backtest Setup: A Deliberately Simple RSI(7) Strategy
I deliberately used a crude setup because I wanted as little as possible between the entry and the result. If I had used a full trading system with smarter exits, position sizing, trend filters, and other rules, it would have been much harder to tell what was actually driving the outcome.
The rule was simple: buy a Nasdaq 100 stock when its 7-period RSI landed inside a defined band, then sell five days later. No stop, no target, no trend filter, and no position sizing. The universe included both current and former Nasdaq 100 constituents, so stocks that were later acquired, delisted, or went to zero were still part of the test.
I ran four RSI bands separately: 20-25, 30-35, and 40-45 on the weak side, plus 70-75 on the strong side. The higher band was there for a reason. I wanted to know whether market context mattered only when buying weakness, or whether it mattered when buying strength too.
The data starts in 2003. That cutoff is deliberate. The dot-com period produced enormous daily and intraday ranges, which were unusually favorable to short-term mean reversion. Including those years would have made the numbers look better, but less representative of the market most traders have dealt with since.
For every trade I also recorded what my exposure model was reading on the day the signal fired. That gave me a clean way to compare the same basic trade across very different market environments.
Baseline Results: Buying Deeper RSI Weakness Performs Better
Before adding market context, here is how the four RSI bands performed on their own.
There is nothing surprising here. The more oversold the stock, the better the five-day trade tended to be. Average profit fell from 0.64% in the 20-25 band to 0.18% in the 70-75 band, and both win rate and profit factor declined with it.
Anyone who has spent much time testing short-term mean reversion has seen some version of this before. Buying a stock at an RSI of 22 is generally a better proposition than buying it at 42. I've written about that previously here and here.
The important number for what comes next is the difference between the best and worst entry bands: 0.46 percentage points per trade.
The Same RSI Signal in Two Different Markets
Now take the strongest entry band, RSI 20-25, and split those same 10,441 trades by what my exposure model was reading when each signal occurred.
The model produces a reading between 0 and 150. Lower readings mean the broader market is less supportive of taking risk. Higher readings mean conditions are more favorable. The reading is calculated the night before, so there is no hindsight involved in sorting the trades this way.
Nothing about the trade changed. It was still the same entry rule, the same five-day exit, and the same universe of stocks.
Taken as a whole, the RSI 20-25 setup had a profit factor of 1.36. In the lowest exposure bucket, profit factor fell to 0.61. That is not simply a weaker version of the same trade; it is a losing one. Only 41.4% of the trades made money, and the average trade went 6.7% against the position at some point during the five-day hold.
At the other end, the top exposure bucket made 1.33% per trade with a 61.3% win rate, and the average adverse excursion was less than 3%.
0.46pp gain from best vs. worst entry band
2.64pp gain from best vs. worst market bucket
5.7×greater impact from market context
Now compare the size of the two effects. Moving from my worst entry band to my best improved average profit by 0.46 percentage points. Moving from the bottom market bucket to the top changed it by 2.64 percentage points.
Market context had more than five times the impact.
Not Just a Dip-Buying Effect: Momentum Entries Respond Too
One RSI band is not enough to tell us much, so I ran the same comparison across all four.
Every band lost money in the bottom bucket and made money in the top bucket. What caught my attention was how similar the separation was across the three weaker RSI bands. The difference between the bottom and top market environments was 2.61 to 2.73 percentage points whether the stock entered with RSI in the 20s, 30s, or 40s.
That tells me the RSI reading and the broader market reading are carrying different information. The depth of the pullback matters, but so does the environment in which you are buying it.
The 70-75 band is useful for another reason: those trades are buying strength rather than weakness. If the exposure model were only identifying conditions that favor dip buying, I would expect the result to fall apart there. It did not. Poor market conditions still hurt the trade, and strong conditions still improved it.
There was one real difference on the momentum side. Dip-buying trades were already profitable when the model was in the 50-75 range. Momentum was still losing 0.15% there and did not turn positive until the reading cleared 75.
Momentum entries needed a stronger market forecast behind them before they worked at all. Dip entries could still work in middling conditions. If you trade breakouts or continuation setups, that is worth paying attention to: your bar for market conditions may need to be higher than the dip buyer's.
Position Sizing by Market Exposure: Do Less, Not Nothing
One approach is to skip trades when the exposure reading is low. That improves the results, but it also throws away information. A low reading does not necessarily mean "do nothing." It may simply mean "do less."
So I tested the 20-25 RSI signals again, this time scaling position size to the exposure reading. A reading of 125 means taking 1.25 times normal size. A reading of 40 means taking 0.40 times normal size. Every signal is still taken; the amount of capital committed changes with the market environment.
Average return per signal increased, the variability of those returns fell, and the strategy used 27% less capital on average.
That is exactly what I want from an exposure model. It puts less money behind signals occurring in poor conditions and more behind the same signals when the broader market is supportive. The signal did not get better. The allocation did.
There is an important limitation here. This is a signal-level calculation, not a portfolio backtest. It does not account for overlapping positions, compounding, capital constraints, or how many positions could actually be held at the same time. I would not treat these figures as portfolio returns.
What they do show is that the market reading can be used for more than simply deciding whether to take a trade. It can also help decide how much risk to put behind it.
Why I Built a Market Exposure Model
I did not build this model to rank stocks. It knows nothing about the stock being traded. It reads SPY.
I built it because I run a few dozen automated systems, and none of them know what the others are doing. Each one follows its own rules and takes trades when those rules say to. Left alone, several can become aggressive at the same time, and occasionally that happens on exactly the wrong day.
I needed something above the individual systems that could answer a different question: how much total market exposure do I actually want right now?
The systems still decide what to trade. The exposure model decides how much aggregate risk I am willing to carry. If my systems are lightly invested and the reading is high, I may add exposure directly through SPY, ES futures, or SSO. If they are heavily invested and the reading is low, I reduce some of it.
This version is designed for daily-bar systems. I use the same basic idea for intraday trading, but with a separate model built around five-minute data. The conditions that support risk over the next several days are not necessarily the same conditions that matter over the next two hours.
That is also why this study interested me. The model had no stock-specific input at all. It knew nothing about any of the Nasdaq 100 companies being traded. Yet a broad SPY reading still separated the outcomes across all four setups, including the one that was buying strength while the other three were buying weakness.
I am not claiming I tested your system. I tested four deliberately simple ones. But if you trade U.S. equities, your trades are still taking place inside the same broad market environment. That is the part worth taking seriously.
The Nightly SPY Forecast: Exposure Reading and Forward Map
I have used this approach in my own trading for years. The daily-bar model runs after the close and gives me an exposure reading for the next session. It is the same reading I used to sort every trade in this article.
What is new is that I am now making more of that process available publicly. Each night, the email includes the current SPY Exposure reading along with the Forward Map, which shows the model's expected path over the next several sessions and the historical range around that forecast.
You do not need to trade my systems or use these RSI rules. Use the information however it fits your own process. Maybe the exposure reading changes your position size. Maybe the Forward Map changes how aggressive you want to be over the next few sessions. Or maybe together they simply give you a better sense of the market environment before you put capital at risk.
I am sending the updated SPY Forecast by email each night, free. Get the free nightly SPY Forecast →
Raw Research is Not a Strategy
This study isolates one effect with four deliberately crude setups. A tradeable system needs exits, sizing, risk controls, and a portfolio that doesn't pile into the same trade on the wrong day. That's what the Trading Time Machine systems are built to do.
Explore the Systems Free SPY ForecastOriginally published on Substack: How to Become a Better Trader: Why Market Conditions Matter More Than Your Entry Signal