TradingHouseGPT is designed to teach those who want to know “Why this. Why that.” Hint: There’s no secret sauce. Looking for the edge to predict price comes natural to many after years of looking at charts. That’s actually not the hard part. The hard part is the execution. And that’s what TradingHouseGPT is. An execution nightmare for the now-outdated Quant algos.

When a session resets, TradingHouseGPT rebuilds context in minutes: load the architecture, drop in your latest screenshots, and the system reconstructs the prior plan from its snapshot—bias, levels, targets, stop, and timebox—then rechecks what changed to confirm “still valid,” adapt, or stand down. Instead of chasing old prompts, you get continuity on demand and a disciplined way to resume execution without losing the thread.

IDE turns your screenshots into a live “what-if” workspace. It stress-tests the trade idea against session context (balance vs trend, volatility regime, news windows), probes alternate entries and stops around HVN/LVN/VPOC and VWAP bands, then refines the plan until the confluence holds up. If a variable shifts—profile shifts, delta flips, liquidity migrates—IDE re-frames the thesis and returns a tightened ticket or a no-trade call. The point isn’t more signals; it’s a harder, institutional-grade filter that keeps only the trades that can survive scrutiny.

PDP is your forward route map for the auction. It lays out how price is likely to traverse the profile—where reclaim should occur, which LVNs act as jump points, how VWAP/Sigma Snap form the acceleration lane, and where trapped inventory is forced to unwind. PDP defines checkpoints (confirm/invalid levels), the expected hand-off between rotations, and a strict timebox, so you follow a pre-drawn path—when to hold, scale, or stand down—instead of reacting candle to candle.