A high-risk, automated DCA strategy that buys leveraged tech/semiconductor ETFs on dips while using bond/dollar hedges and multiple backtested filters to limit risk. It layers safety buys, RSI/volatility gates, and cross-asset checks to balance upside with risk control.

- The bot runs daily and rebalances automatically. - It looks at many assets, with a focus on leveraged tech/semiconductor ETFs (for example, TQQQ, TECL, SOXL) and hedges (e.g., SH for short S&P 500 exposure, UUP/USDU for dollar exposure, BIL for cash-like safety, SHY/IEF for bond context). - Entry decisions rely on several gates: RSI-based thresholds (is the asset overbought or oversold?), moving-average-like checks (is recent return favorable?), and comparisons to other assets (price/return against bonds, dollar strength, or other benchmarks). - When a buy condition is met, the bot places an initial buy for the chosen asset and then uses “safety orders” (stepwise additional buys) to average into the position if price moves against the initial entry, within predefined limits. This is the DCA (buy-the-dip) mechanism. - The algorithm also uses hedges and alternate assets to reduce risk: if the market looks weak, it can tilt toward SH, UUP/USDU, and other defensive proxies; if conditions look favorable, it ramps into leveraged long exposures. There are many gates that decide which assets to buy, how to size them, and when to hedge or reduce risk (e.g., when overbought signals appear, or when a bond proxy looks attractive). - Portfolio balance and risk controls are encoded as weights, top/bottom filters, and thresholds (for example, using standard deviation, drawdown, and moving-average-return signals). - The system groups logic into sections (e.g., “Gas/Hotdogs,” “SOXS vs SOXL,” “safety orders,” etc.) that feed into the overall decision for which asset to own and how many units to buy, and with what hedges to hold. The general idea is: buy selectively, add on dips, hedge risk, and manage size with a disciplined framework rather than emotional trading. - Caveat: this is a complex, high-risk approach using leverage and frequent rebalancing. It’s not suitable for beginners and should be tested extensively in a simulated environment before any real-money use.

Out-of-sample edge: ~29.7% annualized return vs 20.6% for S&P, with Calmar ~1.22 indicating strong risk-adjusted upside. Automated dip-buying in leveraged tech/semis plus hedges aims for higher long-run gains with disciplined risk controls.