Trade Frequency vs Edge Optimizer
Trade Frequency vs Edge Optimizer is a lightweight simulator that shows how trading more often can reduce performance when your edge degrades, even if your strategy looks profitable on paper. You enter three core inputs: Win rate (probability a trade wins), R:R (average reward in “R” units when you win, where 1R equals your risk), and Trades per week (your baseline frequency). The tool then compares two scenarios over a fixed horizon of 200 trades using Monte Carlo simulation.
Each scenario draws hundreds of randomized equity paths (the “Monte Carlo lines”) and highlights the median path, which represents the typical outcome across many possible sequences of wins and losses. Because order matters (streaks happen), Monte Carlo reveals the distribution of outcomes rather than a single expectation.
The two scenarios are:
- Filter more (trade less): Frequency is reduced (e.g., 0.6Ă— baseline). Optionally, the model assumes your win rate improves because you take fewer, higher-quality setups.
- Trade more (overtrade): Frequency is increased (e.g., 2Ă— baseline). Optionally, the model applies a win-rate penalty to represent lower-quality trades, fatigue, missed criteria, or slippage from forcing entries.
Even though both charts use 200 trades, the tool also converts that into an estimated time-to-complete (weeks) using your trades/week. This helps you see the real trade-off: overtrading may finish 200 trades faster, but if edge drops, the median final equity and drawdowns can worsen.
Use the tool by starting with realistic baseline stats (from your journal), running the simulation, and comparing median final equity, max drawdown, and probability of finishing above breakeven. If “trade more” underperforms, you’ve quantified your overtrading cost and can set stricter filters or frequency caps to protect edge.
