It is interesting to consider the evolution of the trading strategy, especially as we work from a July 2025 perspective. By this stage, markets are likely to have experienced significant shifts, particularly following ongoing technological advancements, geopolitical developments, and the increasing maturity of digital assets.
When I think about 'the evolution of my strategy', I envision a journey from a rudimentary approach to a more precise, robust, and adaptive system. Here is a conceptual analysis of how the trading strategy has evolved, incorporating elements relevant to the current market landscape (July 2025):
The evolution of my strategy: adapting to July 2025 markets.
My journey as a trader has been one of learning, adaptation, and continuous development. What began as a relatively simple approach evolved into a sophisticated framework designed to tackle the complexities and opportunities of the 2025 market environment.
Phase One: Foundations (the early days - pre-2023)
Initially, my strategy relied on basic technical analysis and fundamental market assumptions.
Core focus: Identifying clear trends, support/resistance levels, and common chart patterns (such as: head and shoulders, double tops/bottoms).
Indicators: I primarily relied on moving averages (moving average crossover strategies), the Relative Strength Index (RSI) for overbought/oversold conditions, and the Moving Average Convergence Divergence (MACD) for momentum.
Asset classes: I primarily focused on traditional high-liquidity assets such as major currency pairs and large-cap stocks.
Risk management: Basic identification of stop-loss, often at a fixed percentage, and limited position sizing.
Learning: I heavily relied on classical trading literature and online tutorials. My understanding of market microstructure was rudimentary.
Challenges:
Exposure to false breakouts and sudden price volatility.
Difficulty adapting to volatile markets.
Limited understanding of impactful news in the market and its effects.
Emotional biases often led to early exits or holding losing positions for too long.
Phase Two: Absorbing nuances and data (2023 - early 2025)
This phase witnessed a marked shift towards integrating more advanced analytical tools and a deeper understanding of market dynamics. The emergence of advanced data analytics and the increasing reliance on digital assets have significantly influenced this evolution.
Advanced technical analysis: Incorporating more advanced concepts such as Fibonacci retracements/extensions, Elliott Wave Theory (cautiously), and volume pattern analysis to understand liquidity areas.
Inter-market analysis: I began studying the correlations between different asset classes (such as the US Dollar Index and commodities, bond yields, and equities) to gain a broader market perspective.
Quantitative insights: I began integrating basic statistical analysis into the decision-making process. This included studying historical volatility, the probability of certain price movements, and testing strategy variances.
Introduction to algorithmic tools: I explored the basic algorithmic execution of predefined strategies, especially for order placement and stop-loss management, reducing emotional interference.
Core integration (macro and micro): I developed a more robust framework for integrating macroeconomic data (inflation reports, central bank policies, GDP) and company-specific news (earnings, product launches, regulatory announcements) into my analysis, thereby surpassing purely technical signals.
Focus on market structure: I gained a deeper understanding of order flow dynamics, liquidity pools, and the influence of institutional investors.
Early entry into the world of cryptocurrencies: I began cautiously exploring the trading of digital assets, aware of their unique volatility and dependence on specific news triggers and network developments. This required an understanding of concepts such as tokenomics and blockchain fundamentals.
Challenges:
The information overload resulting from the increase in data sources.
Over-optimizing strategies based on historical data.
The market remains prone to surprises from unexpected geopolitical events.
The unique challenges of the cryptocurrency market (security, regulatory uncertainty) have led to new learning curves.