Sanity check on Multi-Strat Algorithm Idea

I'm a software engineer by trade, and have been trading futures on and off for a couple of years now. I want to combine my skills to automate my execution and remove the emotional component. I figured that every decision I make in the market is quantifiable, so the sooner I start formalizing the logic, the better. I'd like to start out with a heuristic-based system before involving Python and ML, as I want to collect clean Order Book data to train a model with anyway. I'm pulling MBO data from Rithmic into Quantower, and plan to write my execution engine in C# using Quantower's API. My idea is to build a system that utilizes lv3 data and order flow dynamics, as this is how I trade manually, looking for passive liquidity consumption, order book imbalances, or momentum at high-volume nodes. The logic will follow 3 steps: * **Identify the Market Regime:** Determine if the market is trending or mean-reverting and identify key price levels (basically just liquidity clusters and previous day value areas/volume profiles). * **Strategy Weighting:** Assign probability weights to multiple strategies based on the detected regime (e.g. if a range-bound state is identified, mean reversion logic trading VAH/VAL to POC will have a higher weight). * **Execution & Confluence:** When price reaches a target level, the system will use confluences such as the VIX, market structure, and order book imbalance to decide on an entry. For example, if it can identify signs of passive absorption or a liquidity sweep, it will use the broader market context to decide which strategy to deploy. This is obviously a high-level overview, and I'm sure there's a ton of issues that I'll discover so feel free to provide a reality check. The overall concept is to build a system that can adapt to shifting market conditions, just as a manual trader would. At work I'm mainly a Python developer, but I'm familiar enough with C# to build the engine. If the initial phase goes well, I'd like to introduce XGBoost or a similar model via a Python bridge to add a secondary bias layer, but for now, it feels like I can hard-code the primary variables in C# while logging snapshots of the data for future training. Any advice and discussion is more than welcome.