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I’m looking for a skilled MQL5 programmer to dive into a project that involves collecting historical forex data and analyzing how major forex pairs, excluding those with JPY, interact with Weekly Open, Daily Open, and Monthly Open price levels. The aim is to evaluate the potential of these levels for a future trading robot by determining how reliably prices respect them, identifying the best timeframe for confirming entries, and measuring typical price movements after reversals. I’m open to your suggestions on timeframes, indicators, or alternative approaches to make the analysis even stronger. There’s also a bonus opportunity for weaving in news, general market seasonality, and fundamental analysis to see how these factors influence performance under different market conditions
For the data collection, you’ll gather historical data for major forex pairs like EUR/USD, GBP/USD, AUD/USD, USD/CAD, USD/CHF, and NZD/USD, covering H1, H4, D1 or other timeframes. The data should include price information, the specified open levels, and technical indicators such as RSI, MACD, and ATR, but feel free to recommend additional indicators that could enhance the analysis. I’d like the data delivered in CSV or Excel format for easy review.The core of the project is analyzing how these forex pairs respect Weekly Open, Daily Open, and Monthly Open levels as potential support, resistance, or reversal points. You’ll quantify how often prices reverse within a range of, say, ±10–20 pips of these levels. You’ll also figure out which timeframe—H1, H4, or D1—works best for confirming entries using signals like candlestick patterns, RSI divergence, or ATR-based methods, and I’m open to your ideas on other timeframes or signals that might perform better. Additionally, you’ll assess how far prices typically move after a reversal, looking at metrics like average pips traveled or the likelihood of hitting 1:1 or 1:2 risk-reward targets. For example, you might test if EUR/USD on H4 reverses at the Weekly Open level 70% of the time within ±15 pips and measure post-reversal runs. If you have creative approaches, like incorporating other key levels or machine learning techniques, I’d love to hear them.
For optimization, you’ll fine-tune parameters like the pip range for reversals, confirmation indicators, or timeframe to maximize reliability while keeping monitoring simulated drawdown. A walk-forward analysis will help ensure the results hold up in both trending and ranging markets. Your report should detail key metrics, including the percentage of successful reversals, the best timeframe for entries, average pips moved post-reversal, and reliability across pairs.As a bonus, I’d like you to explore how these levels perform during high-impact news events, such as Non-Farm Payrolls, FOMC, GDP releases, or ECB/CPI data, and tie in fundamental factors like interest rates, inflation, or economic growth. You’ll also incorporate general market seasonality trends, like performance in January, Q4, or summer months, which often show unique patterns in forex markets. For instance, you could analyze whether reversals at Daily Open levels are more reliable during low-volatility news periods, assess level respect during seasonal periods like a strong USD in Q4, or evaluate how high interest rate differentials affect reliability. A separate report should compare level performance across news versus non-news periods, seasonal trends, and fundamental conditions, offering clear insights.
You’ll deliver the historical data in CSV or Excel format, along with detailed analysis reports covering level respect, timeframe suitability, and post-reversal price movements. Optimized parameters are essential. For the bonus, include a report on how news, seasonality, and fundamentals impact performance. Optionally, you can provide a prototype script or indicator to highlight key levels for future Expert Advisor development.
The budget isn’t fixed and needs to be discussed upfront. I’m accepting bids and will evaluate them based on your experience, proposed approach, including any creative suggestions, and whether you include the news, seasonality, and fundamental analysis components.
To apply, please share your portfolio, relevant MQL5 projects, and a detailed bid with your proposed budget and timeline. Include a brief explanation of how you’ll tackle this project, emphasizing your experience with analyzing key price levels like Weekly, Daily, or Monthly Opens, as well as integrating news, seasonality, and fundamental analysis. Let me know how you’ll source and incorporate seasonality and fundamental data, whether through APIs or historical analysis, and how you’ll measure level respect, timeframe suitability, and post-reversal movements. If you have suggestions for better timeframes, indicators, or approaches, like alternative levels or advanced techniques, please highlight them in your proposal.