Overview: This is a long-only AI trading robot designed for a defined ladder of energy-sector equities: BP, CNQ, EQNR, WMB, VLO, MPC, and OXY. The strategy applies a systematic daily-chart pattern framework to identify temporary pauses in price movement and participate when predefined continuation conditions are met. The universe follows an Energy Cap Ladder, moving from larger, more liquid exposures toward smaller or more volatile variants, where liquidity constraints and price dispersion can become materially more significant. Rickshawman is rule-driven rather than discretionary: it does not rely on analyst Buy/Sell scores, earnings predictions, or news-based speculation, and every position is governed by explicit entry, protection, and exit logic.
The 60-Minute ML layer serves as an intraday analytical and risk-management component around Rickshawman’s daily-pattern thesis. While the primary setup originates from the daily chart, 60-minute market data can be used to evaluate shorter-term price structure, volatility, momentum, and trading conditions before and during an active position. Machine-learning outputs are treated as supporting signals rather than standalone trade instructions: the daily Rickshawman setup remains the strategic anchor, while the 60-minute layer can help distinguish stronger from weaker setups, improve timing, and identify deterioration in the conditions supporting an open trade.
An inverse ETF (Exchange-Traded Fund) is designed to provide investment results that correspond to the inverse of the daily performance of a specified index or benchmark. Leveraged inverse ETFs seek to amplify this inverse exposure by a predetermined multiple, providing a tactical instrument for strategies designed to respond to declining market conditions.
The trading robot incorporates ERY (Direxion Daily Energy Bear 2X ETF) as its inverse market exposure component. ERY seeks to deliver 200% of the inverse (-2X) of the daily performance of the Energy Select Sector Index, providing leveraged inverse exposure to the U.S. energy sector.
The inclusion of ERY enables the trading strategy to take tactical positions during periods of weakness or negative momentum in energy-sector equities. This expands the robot’s ability to operate across different market environments and provides an additional instrument for managing directional market exposure within the overall strategy.
As a leveraged inverse ETF, ERY resets its exposure on a daily basis. Due to the effects of daily rebalancing, leverage, market volatility, and compounding, its performance over periods longer than one trading day can differ materially from two times the inverse of the cumulative performance of the underlying index. These effects may become more pronounced during periods of elevated volatility.
Accordingly, ERY is utilized as a tactical trading instrument rather than a long-term inverse position, with its exposure managed within the robot’s broader trading and risk-management framework.
Rickshawman is a pattern-based algorithm focused on the daily Rickshawman candlestick formation—a candle characterized by a relatively small real body with shadows extending on both sides. In practical terms, the pattern represents a temporary equilibrium or “pause” after price movement, when neither buyers nor sellers have established decisive control. The robot detects this condition, evaluates whether it satisfies its predefined trading criteria, and waits for confirmation before entering a long position. Once a trade is active, the system follows predetermined protection and continuation rules rather than attempting to predict every subsequent market move. Because the setup can occur relatively frequently, Rickshawman is designed to be more active than strategies dependent on rare structural events.
The strategy combines daily candlestick-pattern recognition, long-only directional exposure, systematic confirmation rules, 60-minute ML analysis, volatility-aware risk controls, and deterministic exit logic. Its technical foundation is deliberately separated from fundamental Buy/Sell scoring: analyst recommendations, earnings forecasts, headlines, and discretionary interpretations are not the primary triggers for trades. Instead, the robot seeks repeatable price behavior around periods of temporary market indecision. The energy-only universe provides thematic consistency while still creating exposure to different parts of the sector through BP, CNQ, EQNR, WMB, VLO, MPC, and OXY.
The quantitative framework should define explicit thresholds for entry confirmation, maximum position size, stop distance, maximum loss per trade, portfolio exposure, volatility eligibility, liquidity requirements, and exit conditions. These thresholds should be calibrated separately where necessary because the seven securities do not share identical volatility, liquidity, or gap-risk characteristics. Position sizing should therefore be risk-normalized rather than based solely on equal capital allocation. Any backtested thresholds should also incorporate transaction costs, slippage, overnight gaps, and realistic execution assumptions before being treated as economically meaningful.
Rickshawman is built around the idea that a temporary daily price equilibrium can create a measurable opportunity when followed by renewed directional movement. Its advantage is not an assumption that every Rickshawman candle predicts continuation or reversal; rather, the strategy attempts to apply the same entry and risk rules repeatedly across a controlled energy universe. Risk is attributable to energy-sector concentration, long-only market direction, commodity-price sensitivity, company-specific events, overnight gaps, pattern failure, volatility expansion, execution slippage, and liquidity differences across the Energy Cap Ladder. Smaller-cap or less-liquid variants should be expected to carry materially greater volatility and liquidity risk. The robot therefore treats the Rickshawman pattern as a structured opportunity—not a guarantee—and relies on disciplined protection and exit rules when the expected continuation fails to materialize.
Maximum Open Positions: Medium, allowing for diversified exposure while managing concentration risk.
Robot Volatility: Low, attributed to the strategic entry after minor pullbacks and careful position management.
Universe Diversification Score: High, indicating a broad array of instruments to hedge against sector-specific downturns and enhance profit opportunities.
Optimal Market Condition High: If the current market volatility is Medium, then you should use the Best Robots in a Medium Volatility Market (VIX is Medium - this indicator is coming soon).
Disclaimer: Disclaimers and Limitations
Simulated Performance: All simulated performance results are derived solely from real-time calculations using historical data. Algorithms receive minute-by-minute historical prices and other data from Morningstar and generate trades in real time based on these historical inputs, effectively eliminating any hindsight bias.
Actual Performance: All actual performance results are derived solely from real-time calculations using current data. Algorithms receive minute-by-minute current prices and other data from Morningstar and generate trades in real time based on these current inputs, effectively eliminating any hindsight bias.
Gross Performance: Gross performance results do not deduct any fees or expenses. These results reflect the total returns generated by the AI Robots without considering the costs associated with accessing the service.
Net Performance (current performance chart): Net performance results deduct fees to provide a more accurate representation of returns experienced by the user. These deductions can include: Model Fee Deduction: Net performance results may deduct a model fee equivalent to the highest subscription fee charged to the intended audience. Actual Subscription Fees: Net performance results may also deduct the actual subscription fees paid by the user for access to AI Robot
This Robot is recommended to be used when the markets are growing in general. The core algorithm makes only long The core algorithm makes only long