Esta es una de las estrategias que lo hicieron millonario (con backtest)

Analysis of Iván Sherman's Trading Strategy

Introduction to Iván Sherman

• Iván Sherman is a well-known Spanish-speaking trader, recognized for winning a trading championship with nearly 500% profitability.

• He is characterized as charismatic, clear, and direct in his presentations.

Overview of the Trading Strategy

• The strategy discussed involves using the BX index to enter and exit positions in the S&P 500.

• The video will break down this strategy step-by-step for better understanding and explore potential improvements.

Key Conditions for Trade Execution

• Entry Conditions:

• The opening price of the BX must be above the Bollinger Band over a 20-day period.

• The closing price of the BX must be below its Bollinger Band.

• The distance between the opening and closing prices must exceed 10%.

• Exit Condition:

• A sell order is executed when the BX closes below its 20-day moving average.

Mathematical Foundation of the Strategy

• The entry conditions are strictly defined, allowing for no interpretation—it's entirely mathematical. This ensures replicability in trading decisions.

• To calculate if the distance exceeds 10%, use: (Close - Open) / Open ≥ 0.10. This formula quantifies entry criteria effectively.

Logic Behind the Trading Approach

• The strategy capitalizes on implied volatility measured by BX, which tends to exaggerate market fear during negative news or uncertainty at market openings. If this fear subsides during trading hours, institutional buying often stabilizes prices. Thus, entering trades under these conditions can be advantageous.

Practical Application and Results

• An example illustrates how to identify an entry point based on price movements relative to moving averages; trades are opened when specific patterns emerge in price action around Bollinger Bands and moving averages.

• A Python implementation of this strategy yielded results from 1991 to 2025 with an ending equity of $12,716 and a return rate of approximately 17.64%. Higher trade volumes correlate with increased profits when strategies perform well; however, only a few trades occur due to daily execution limits leading to a maximum drawdown of about 14%.

Analyzing Drawdown and Strategy Adjustments

Understanding Drawdown Patterns

• The largest drawdown identified is 118%, indicating significant losses during downturns, particularly when there are large retracements in the market.

• Strategies to mitigate these drawdowns include entering trades only during specific bearish periods of the S&P 500 or applying filters such as ensuring the closing price is above a 200-day moving average.

Results of Strategy Implementation

• After implementing the new strategy from 1991 to 2025, total gains decreased slightly to 11,737 due to fewer trades (42), but commission costs were reduced.

• Key metrics improved: Profit factor increased to 2.12, maximum drawdown reduced to 9.9%, and winning percentage rose from 77% to 78.57%.

Visualizing Performance Improvements

• The updated strategy avoids entering trades during certain high-risk periods, leading to a lower overall drawdown despite some remaining risks.

• Notably, the strategy did not engage during major downturns like COVID or other significant market corrections since 2017.

Future Directions for Strategy Enhancement

• The goal is to explore additional effective filters and complementary strategies that can offset weaknesses in this approach during its less favorable moments.

Capturing Market Behavior

• This simple yet effective strategy capitalizes on human behavior—specifically exaggerated fear in market reactions—allowing for profitable entries when markets stabilize after panic events.