Numerical Price Prediction Challenge

COMPARING TECHNICAL ANALYSIS TO FLIGHT DYNAMICS

Technical analysis in forex trading involves studying historical price movements, chart patterns, and possibly indicators (such as moving averages, RSI, Fibonacci retracements, etc.) to predict future market behavior. It's grounded in the idea that price action reflects market psychology and that patterns repeat due to human behavior. Flight dynamics, on the other hand, is the study of how aircraft move through the air, governed by physical principles like lift, drag, thrust, and weight, often modeled with precise equations and real-time data to predict and control flight paths.

It's my contention that, as a forex trader, one can draw parallels between the two by noting similarities in how both fields deal with predicting and navigating complex, dynamic systems:

1. Predicting Trajectories: In flight dynamics, engineers predict an aircraft's path based on current conditions (e.g., wind, velocity) and historical data (e.g., aerodynamic models). Similarly, technical analysts predict price movements by analyzing past price action and current market conditions, using tools like trend lines or support/resistance levels to forecast the market's "trajectory."
2. Feedback and Adjustments: Pilots use real-time feedback (e.g., from instruments) to adjust an aircraft's course, much like traders use indicators (e.g., MACD or Stochastic Oscillator) to adapt to shifting market conditions. Both require responsiveness to changing variables to avoid "crashing" (in trading, significant losses).
3. Momentum and Trends: In flight dynamics, momentum (mass times velocity) influences how an aircraft behaves. In technical analysis, momentum indicators like RSI or MACD measure the strength and direction of price trends, helping traders decide whether to "ride" a trend or anticipate a reversal, akin to managing an aircraft's momentum during flight.
4. External Forces: Aircraft face external forces like turbulence or wind shear, which require predictive adjustments. Similarly, markets face external shocks (e.g., economic news or geopolitical events) that technical analysts account for, often combining fundamental analysis to contextualize price movements, much like pilots use weather data.

My emphasis on both trend and counter-trend strategies along with the influences of market psychology aligns well with the idea of navigating markets in the way a pilot adjusts to turbulent air. Similarly, George Soros’s concept of "reflexivity" (market perceptions influencing reality) could be likened to how a pilot's decisions affect an aircraft's path.

Flight dynamics, being a physics-based field, might feel too mechanistic for most traders to invoke, as trading heavily involves human emotion and irrationality, which aircraft don’t exhibit; which might help to explain the rather niche nature of my analogy.

 

It's also my contention that one can draw parallels between technical analysis and numerical weather prediction (NWP) methodologies used by meteorologists as both fields involve forecasting complex, dynamic systems using data-driven models.

But even after having made this statement, I recognize also that though both aim to predict future states of complex systems, their methodologies differ significantly. For example, technical analysis in forex trading focuses on historical price data, chart patterns, and indicators (e.g., moving averages, RSI, Fibonacci levels) to predict future price movements, assuming that market psychology drives repeatable patterns. On the other hand, the NWP used by meteorologists relies on mathematical models (e.g., primitive equations) that simulate atmospheric dynamics based on current and historical data from satellites, radars, and weather stations, solving complex fluid dynamics equations to forecast weather.

So, while technical analysis is in a manner empirical, it nonetheless evaluates price and volume data without a physical model of markets, whereas NWP uses physics-based equations (e.g., Navier-Stokes) to model atmospheric behavior. In other words, they rely on different data and models. For instance, forex technical analysis uses simple tools like trend lines and candlesticks, while NWP relies on complex models like GFS and ECMWF, and this kind of gap in sophistication makes a direct analogy somewhat problematic

Moreover, weather systems, though chaotic, are governed by physical laws, allowing NWP to achieve high accuracy for short-term forecasts (e.g., 80% for 5-day forecasts). Forex markets, driven by human behavior, economic events, and sentiment, are less predictable, with only 2% of retail traders consistently profiting from predictions. (But I should note that the success rate of my NWP inspired system is much higher—somewhere north of 80% generally speaking.

Also, technical analysts use indicators and chart patterns (e.g., Ichimoku, Elliott Waves), while NWP employs supercomputers (e.g., NOAA’s WCOSS with 5.78 petaflops) to process vast datasets and run ensemble models. I should note as well that reputable traders like Paul Tudor Jones or George Soros focus on market psychology, macroeconomic trends, or reflexivity rather than physical modeling, making NWP an unlikely analogy.

Other marked differences include the following:

• Domain Differences: Forex markets are driven by human sentiment, unlike the physics-based atmosphere, making psychological or statistical analogies (e.g., to behavioral finance) more intuitive.
• Accessibility: NWP requires advanced mathematical knowledge, whereas technical analysis is accessible to retail traders using platforms like MetaTrader.
• Practical Focus: Traders prioritize actionable strategies over abstract analogies, and weather data is more relevant to commodity trading than forex.

But despite the lack of direct comparisons, I am still inclined to draw parallels between technical analysis and NWP, as both involve forecasting under uncertainty. Here are my analogies:

1. Data Assimilation and Initial Conditions:
   • In NWP, meteorologists assimilate diverse data (e.g., from Doppler radar, satellites, radiosondes) to establish the atmosphere’s current state, which initializes models. Similarly, technical analysts use price, volume, and indicator data to assess the market’s current state, setting the basis for predictions.
   • Example: A trader using Bollinger Bands to gauge volatility is akin to a meteorologist using temperature gradients to predict storm formation—both assess current conditions to infer future behavior.
2. Pattern Recognition:
   • NWP models identify patterns in atmospheric data (e.g., pressure systems) to forecast events like hurricanes. Technical analysis relies on chart patterns (e.g., head-and-shoulders, triangles) to predict price movements, assuming historical patterns repeat.
   • Example: Elliott Wave theory, which identifies repetitive price cycles, could be likened to meteorologists recognizing recurring weather patterns like El Niño.
3. Iterative Forecasting:
   • NWP uses time-stepping, where models iteratively calculate future atmospheric states based on current rates of change. Technical analysts iteratively update predictions as new price data emerges, adjusting strategies based on moving averages or RSI shifts.
   • Example: A trader adjusting a stop-loss based on a new candlestick pattern is similar to a meteorologist refining a forecast as new satellite data arrives.
4. Uncertainty and Probabilistic Outputs:
   • NWP employs ensemble models to account for uncertainty, providing probabilistic forecasts (e.g., 70% chance of rain). Technical analysis often yields probabilistic signals (e.g., a breakout above resistance suggests a 60% chance of an uptrend), though less formalized.
   • Example: A trader using RSI divergence to anticipate a reversal might assign probabilities to outcomes, akin to meteorologists’ probabilistic storm warnings.
5. Chaos and Limitations:
   • Both systems are chaotic, with small initial errors amplifying over time. In NWP, errors double every five days for variables like temperature. In forex, small misjudgments in support levels can lead to significant losses due to market volatility.
   • Example: A trader misreading a false breakout is comparable to a meteorologist underestimating a storm’s path due to model instability.

I would also note that both forex analysis and weather forecasting increasingly use machine learning. Forex traders apply deep learning models (e.g., LSTM, CNNs) to predict currency movements, while meteorologists use AI to enhance NWP accuracy.

So then, in the final analysis, I still hold that there exist conceptual similarities between technical analysis and NWP, such as data assimilation, pattern recognition, and managing uncertainty, which I have found helpful in designing a forex day trading system that I believe results in almost unparalleled success.

 

A commonly cited statistic is that approximately 70-80% of retail forex traders lose money, meaning only about 20-30% are profitable. The most frequently referenced sources for these statistics include:

• Regulatory disclosures from forex brokers in jurisdictions such as the EU, UK and Australia who are required to publish quarterly risk warnings typically showing that 70-85% of retail accounts lose money.
• Data published by the European Securities and Markets Authority showing that around 74-89% of retail CFD accounts (which includes forex) lose money across different brokers.

However, this data typically doesn't distinguish between serious traders and those treating forex as gambling. But in any case, indicating that 11% to 30% of retail forex traders are profitable does not jibe with the 2% figure quoted in my previous post—so let me explain the discrepancy…

The more pessimistic 2% figure referring to retail forex traders who are consistently profitable comes from supposedly conclusive evidence which found that only approximately 2% of day traders can actually consistently turn a profit.

More specifically, academic research conducted by Brad Barber, Terrance Odean and their colleagues, who studied day trading performance extensively, consistently showed that only 1%-3% of day traders can continuously outperform the stock market, with only 13% maintaining consistent profitability over six months, and a mere 1% succeeding over five years.

https://www.quantifiedstrategies.com/day-trading-statistics/
https://www.newtrading.io/is-day-trading-profitable/

 

REVISED DESCRIPTION OF NPP:

My system for trading financial instruments online—Numerical Price Prediction or NPP—was fully realized on Sunday, July 20, 2025. This system is a quantitative, data-driven methodology that combines principles from flight dynamics, numerical weather prediction, cycle theory and Edgar Peters' fractal market hypothesis to identify high-probability setups with statistical edges.

Again, similar to how meteorologists use numerical weather prediction models, I employ physics-like rules and pattern recognition to detect recurring signals based on key technical levels and past price behavior across multiple timeframes.

This disciplined system emphasizes patience and selectivity, executing trades only when risk/reward ratios and technical conditions heavily favor success—maintaining the statistical advantage of "the house" rather than the gambler—a notion I've heard most extensively expressed by Gareth Soloway, chief strategist at Verified Investing.

By integrating real-time data analysis with mathematically justifiable entry and exit points, the NPP system aims for consistent profitability through short-term, high-probability trades while minimizing exposure to market noise and emotional biases, targeting an 80-100% daily success rate through rigorous precision and disciplined risk management.

It's been suggested to me that my approach, which relies on technical analysis, cannot honestly be compared to something like flight dynamics or numerical weather prediction because technical analysis is grounded in the idea that price action reflects market psychology and that patterns repeat due to human behavior, whereas flight dynamics is governed by physical principles like lift, drag, thrust and weight, often modeled with precise equations and real-time data to predict and control flight paths.

But that's exactly the point.

NPP does not concern itself with fear, greed, cognitive biases, emotional exuberance or social dynamics. Rather, it simply quantifies price action in numerical terms, without regard to what emotions might or might not be driving or motivating any of the observed patterns.

So, in the same way that engineers predict an aircraft's path based on current conditions (e.g., wind, velocity) and historical data (e.g., aerodynamic models), Numerical Price Prediction anticipates price movements by analyzing past price action and current market conditions, using tools like trend lines, horizontal and diagonal support/resistance levels and historically established price ranges to forecast an asset's most probable trajectory.

Moreover, just as pilots use real-time feedback (e.g., from instruments) to adjust an aircraft's course, NPP uses the above-mentioned measures to do something similar—adapting to shifting market conditions in response to changing variables to avoid "crashing" (i.e., significant losses).

Also, in flight dynamics, momentum (mass times velocity) influences how an aircraft behaves; and in NPP, rate of change (distance divided by time) is used to measure and evaluate the strength and direction of price trends, helping traders decide whether to ride a trend or anticipate a reversal, akin to managing an aircraft's momentum during flight.

And just as aircraft use weather data to make predictive adjustments to external forces like turbulence or wind shear, NPP modifies trade decisions in the wake of external shocks (e.g., economic news or geopolitical events) by contextualizing price movements mathematically.

Flight dynamics, being a physics-based field, might feel too mechanistic for most traders to invoke. But since NPP has no interest in involving human emotion and irrationality (which aircraft don't exhibit) in its calculations, I continue to feel comfortable stating that Numerical Price Prediction owes its documented success record—short as that might be—at least in part to its incorporation of flight dynamics-like principles...



As stated twice already, it's also my contention that one can draw parallels between Numerical Price Prediction (NPP) and the numerical weather prediction (NWP) methodologies used by meteorologists, which is actually how my system got its name, since both fields involve forecasting complex, dynamic systems using data-driven models.

But even after having made this statement, I recognize it could be argued that the NWP used by meteorologists relies on mathematical models (e.g., primitive equations) that simulate atmospheric dynamics based on current and historical data from satellites, radars and weather stations; solving complex fluid dynamics equations to forecast weather—whereas technical analysis assumes that psychology drives repeatable patterns.

But again, NPP has no interest in market psychology...only in focusing on historical price data, chart patterns and indicators such as moving averages and moving average envelopes.

Yes, it's true that technical analysis evaluates price and volume data without a physical model of markets, as opposed to NWP, which uses physics-based equations (e.g., Navier-Stokes) to model atmospheric behavior. But even so, this doesn't necessarily mean that technical analysis cannot also turn to mathematical patterns to account for forces like pressure (momentum), viscosity (liquidity and volatility), and external forces—no matter the gap in sophistication between the simple tools use in technical analysis, like trend lines and candlesticks, and the complex models use in weather forecasting, such as the Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF).

The fact that being governed by physical laws enables NWP to achieve high accuracy for short-term forecasts (e.g., 80% for 5-day forecasts) in comparison to the approximately 2% of retail day traders able to profit consistently from their predictions made in markets driven by human behavior, economic events and sentiment, doesn't really have any bearing on or relevancy to NPP, where psychology, external events and intuition play no role, resulting in a typical 80-100% daily success rate stemming from trade-decisions made based strictly on the numbers...



So then, similar to how meteorologist use NWP to assimilate diverse data (e.g., from Doppler radar, satellites, radiosondes) to establish the atmosphere's current state, which initializes models—NPP uses technical analysis tools to assess the market's current state, setting the basis for predictions.

And just as NWP models identify patterns in atmospheric data (e.g., pressure systems) to forecast events like hurricanes, NPP relies on replicable chart patterns to predict price movements, assuming historical patterns repeat.

Moreover, in the way NWP uses time-stepping, where models iteratively calculate future atmospheric states based on current rates of change, NPP iteratively updates predictions as new price data emerges, adjusting strategies based on shits in moving averages, price ranges and rates of change.

Additionally, similar to the manner in which NWP employs ensemble models to account for uncertainty, providing probabilistic forecasts (e.g., 70% chance of rain), NPP often yields probabilistic signals (e.g., a breakout above resistance suggesting a 70% chance of an uptrend), though less formalized.

I would note also that both technical analysis and weather forecasting increasingly use machine learning, with traders applying deep learning models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to predict currency movements, while meteorologists use AI to enhance NWP accuracy.

So then, in the final analysis, I still hold that there exist conceptual similarities between technical analysis and NWP, such as data assimilation, pattern recognition, and managing uncertainty, which I have found helpful in designing a trading system that I believe results in almost unprecedented success.

1st Nine Days:

 

Now that you've optimized your trading system, it's time to optimize your routine. Take anecdotal notes on the stuff you do so you stop leaving things out. Keep extending and modifying the list until it's set in stone.

1. Check the economic calendar to see what's on the horizon for the next 24 hours.
2. Review your OANDA MT4 1-Hour Monthly Price Range II chart profile to get the lay of the land.
3. Open your OANDA MT4 1-Minute SUPER FOCUS chart profile so you'll be alerted of possible trade setups, even though you won't be monitoring the charts yourself personally.
4. At 6:00 PM on Sunday and 4:00 PM the rest of the week, open up your NADEX platform and see if there are any binary option or knock-out contracts that might justifiably be purchased based on your Tradiso Group MT5 14-Minute Price Flow commodity and USA index chart setups, which will also need to be pulled up.

 

1. Check the economic calendar to see what's on the horizon for the next 24 hours.
2. Review your OANDA MT4 1-Hour Monthly Price Range II chart profile to get the lay of the land.
3. Open your OANDA MT4 1-Minute SUPER FOCUS chart profile so you'll be alerted of possible trade setups, even though you won't be monitoring the charts yourself personally.
4. At 6:00 PM on Sunday and 4:00 PM the rest of the week, open up your NADEX platform and see if there are any binary option or knock-out contracts that might justifiably be purchased based on your Tradiso Group MT5 14-Minute Price Flow commodity and USA index chart setups, which will also need to be pulled up.
5. Don't forget to make sure everything that needs recharging was plugged in by 3:00 PM at the latest.

 

If you're suspending trading a given asset until after the release of certain economic data, literally put the time the information is scheduled to be released on the chart itself...