Así opera el trader que gestiona 12M$
Introduction to Sergi Sánchez and the Apollo System
Overview of Sergi Sánchez
- This segment introduces Sergi Sánchez, known for his work with Ser San Sistema and Darwin Seo, boasting over 7 years of experience and managing 11 million.
Concept of Reverse Engineering
- The term reverse engineering is explained, likening it to extracting documentation from a physical piece, such as creating plans from a pump's impeller.
Understanding the Apollo System
Objective of Analysis
- The goal is to deduce how Sergi's system, the Apollo System, generates impressive metrics without knowing its internal rules or logic.
Methodology
- The analysis will utilize available sub-metrics to reconstruct the financial "crime scene," aiming to understand what makes this system effective.
Details on Trading Strategy
Focus on Long Positions
- The discussion centers on the long position strategy within the Apollo system, specifically targeting trades in mini NASDAQ futures.
Trading Characteristics
- It operates as a swing trading system that keeps positions open overnight and only trades during regular hours (8:30 AM - 3:15 PM Chicago time).
Trading Mechanics and Market Behavior
Entry Timing
- Trades are initiated during periods of high volatility identified on charts, allowing for strategic entry points based on market behavior.
Anti-Trend Strategy
- The Apollo system is characterized as an anti-trend strategy that enters trades when prices deviate from their average, aiming for price reversion back to the mean.
Performance Metrics Analysis
Profit Factor Insights
- Over five years (June 2020 - June 2025), the system shows a profit factor of 2.2, indicating strong performance typically associated with higher time frames.
Accuracy Rate
- With an accuracy rate of 59.7%, it falls within expected ranges for mean reversion systems but suggests potential adjustments based on take profit distances.
Trade Outcomes and Equity Curves
Trade Performance Characteristics
- Notably, this system has larger take profits compared to losses; winning trades outnumber losing ones significantly (9 wins vs. 6 losses).
Implications for Strategy Success
- A higher average win ratio indicates that successful trades tend to yield more significant returns over longer durations, aligning with historical trends in asset growth.
Conclusion: Strategic Goals in Trading
Targeting Price Movements
- The primary aim is to capitalize on moments when prices diverge from their averages while allowing profitable movements to run longer due to historical bullish trends in assets like NASDAQ.
Results and System Overview
Performance Analysis of the Trading System
- The trading system shows mixed results, with some trades closing at target objectives while others result in losses. Notably, there are several entries that seek to capitalize on downward trends.
- The system employs a time filter to limit entry points and utilizes various indicators such as Kelner, Donchian channels, Bollinger Bands, and RSI to identify deviations for trade entries.
- A risk management strategy is highlighted, adjusting exposure based on market conditions. It avoids entering trades during strong bearish trends that could trigger stop-loss orders.
- Key metrics include a win rate of 58.93%, a profit factor of 1.93, and a maximum drawdown of 13%. These figures are comparable to another system discussed earlier in the video.
Comparative Metrics
- The average gain per winning trade is 256 units while the average loss per losing trade is -190 units. This indicates a favorable risk-reward ratio despite some losses.