The last post covered what grid trading is and how it differs from averaging down. This post goes deeper — into what actually went wrong with my first attempt at a grid strategy, and what that failure revealed about how grid trading needs to be designed.
How the First Version Was Built
My initial grid strategy started buying at 4.5% below the recent high and continued in 25 stages all the way down to 15.0% below. Rather than distributing equal amounts across each stage, I weighted the later stages much more heavily. The reasoning seemed sound: "once the price has fallen far enough, buy aggressively to bring the average cost down effectively."
The exit rules were a mix of two approaches. For the earlier stages (roughly stages one through eight), I used a symmetric percentage target: take profit at +3% above the average cost, cut the loss at -3%. For the later stages, I switched to a fixed dollar target: exit with a specific amount of gain, or exit at a fixed dollar loss. The same +3%/-3% logic kept applying through the early stages, but once the position grew in the later stages, the potential loss at the stop-loss level grew with it — while the take-profit target stayed fixed.
The Problem in Numbers
This asymmetry becomes obvious once you work through the numbers. In the early stages, the risk-reward ratio is a reasonable 1:1. But as the weighting ramps up in the later stages, the structure quietly shifts into something unfavorable: the upside target is locked at a fixed dollar amount, while the downside exposure keeps growing with the position size. In simple terms, the profit ceiling was fixed while the loss floor kept falling.
This is a classic trap in grid trading. When all the focus is on lowering the average cost, it's easy to lose track of how the exit math changes as position size grows. Once you actually model the numbers — cumulative shares, weighted average cost, profit at take-profit, loss at stop-loss — stage by stage, the flaw is unmistakable.
The fix required two things working in tandem. First, the exit targets had to match the position size at each stage, keeping the risk-reward ratio consistent across the full grid, not just in the early stages. Second, the reference point for calculating risk-reward had to be the weighted average cost across all filled stages — not the entry price of the current stage alone.
The Second Problem: Starting Too Deep
There was another issue compounding all of this: the grid didn't start buying until the price was already 4.5% below the recent high. In theory, this seemed like a reasonable filter — "wait for a meaningful pullback before entering." But in practice, stocks with real momentum rarely give you a 4.5% pullback. They dip shallower — maybe 1% to 2% — and then break higher again. The 4.5% threshold meant the system was, in effect, sitting out the best opportunities and only entering the ones where momentum had already faded.
Later, I moved the starting entry to 1.0% below the recent high. That change alone meaningfully increased the frequency of actual entries, and brought the strategy into alignment with the actual behavior of the stocks I was targeting.
Always Run the Numbers First
The lesson that came out of all this was simple but easy to forget: no matter how logical a strategy sounds in your head, you have to actually run the numbers before putting real money behind it. The weighting structure felt right intuitively — "buy more when the price has fallen further" — but once modeled out stage by stage, it was clearly producing unfavorable risk-reward ratios in the very stages that carried the most capital.
Running a proper simulation means calculating, for every possible exit point across every stage, exactly how much you make if the trade works and exactly how much you lose if it doesn't. If you skip that step, you won't know your strategy is broken until you're already sitting in the loss.
Today's Investing Insight — Why Risk-Reward Ratio Matters More Than Win Rate
Most traders fixate on win rate — how often their trades are profitable. But what actually determines long-term performance is the combination of win rate and risk-reward ratio. A strategy that wins only 40% of the time can still be profitable over the long run if the average winner is twice as large as the average loser. Conversely, a strategy with a 70% win rate will lose money if the average loss is large enough to wipe out several wins in a single trade. In grid trading, where capital compounds across multiple stages, risk-reward calculations need to be done against the weighted average cost of the full position — not just the entry price of the current stage. Getting that calculation wrong is one of the most common and costly mistakes in multi-leg strategies.
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This post documents a personal journey of building an algorithmic trading system and is not a recommendation of any specific strategy. The numbers and structure described here are for illustrative purposes only and do not guarantee results in any market condition. All investment decisions and their outcomes are the sole responsibility of the investor.
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