🇪🇺🇺🇸 EURUSD

AI Trade Report

EURUSD || Sell Limit @1.11404 29.09.2023 08:29:04 UTC || Close @1.05802 29.09.2023 08:29:11 UTC || +5.02% [TEST 15]

Trade Report

The EURUSD trade opened at 1.11404 and closed at 1.05802, resulting in a substantial drop of approximately 560 pips. This short position resulted in a sizeable profit for our trading algorithm, a testament to its market insights and predictive capabilities.

Before the trade was initiated, the stochastic signal over the 1 minute timeframe was at 45.13 whilst the 15 minute stochastic signal was at 86.49, indicating the market was largely overbought and that a reversal was likely. This gives credence to the decision to enter a sell limit order.

The image below provides a focus on the 1-minute timeframe where we can see both the price action and the RSI.

The RSI, another popular momentum oscillator, also indicated the market was moderately overbought as the value was at 47 at trade open and closed slightly lower at 46.25, a bearish divergence signal that aligns with our short position.

Moving Average convergence divergence (MACD), a trend-following momentum indicator, also signaled bearish momentum. From the trade open, MACD signals on the 1 minute, 1 hour, and 15-minute timeframes have remained relatively stable, suggesting the strength of the trade to the downside.

Let’s take a closer look at the price chart with momentum indicators – MACD and CCI, alongside volatility indicators – Bollinger Bands and Average True Range (ATR). This image is shown below.

One of the most used indicators for measuring volatility is the ATR. At open, the ATR was 0.00008 on the 1 minute chart, and remained the same at close, implying that the market was experiencing relatively stable volatility throughout the trade.

The trade was also validated by the Commodity Channel Index (CCI), which moved from -43.03 to -48.16 on the 1-minute timeframe – another strong signal of the bearish trend.

The Bollinger Bands mirrored these findings, with the BB main moving from 1.05807 to 1.05806 on the 1-minute timeframe, reflecting a bearish momentum in line with our short position.

Our automated trading system, FundGPT, also takes into account long-term trends before making a trade. Importantly, the indicators on the 1 hour and 15-minute charts align with the trade. The stochastic signals on the 1-hour chart and 15-minute chart both indicated overbought conditions, supporting the trade decision.

In the grand scheme of things, the trade on the EURUSD pair was executed with precision, utilising both short-term and long-term indicators to maximise profitability. The assortment of technical indicators and the efficiency of our autonomous trading system combined to make this trade a successful one.

This deep dive into technical indicators of this trade highlights the intelligence of FundGPT. We hope this report provides a clear and complete overview of the market conditions when the trade was executed and how all of the technical indicators converged to point towards a profitable short position.

AI Training & What We Learned

In the continuous pursuit of refining our proprietary FundGPT trading system, each executed trade provides an opportunity to learn, adapt, and enhance our algorithm’s trading acumen, even when the trades are profitable.

Let’s take the recent EURUSD trade for instance. Despite generating a substantial profit, the dynamic data points represented in the numbers can offer pivotal insights. The RSI at trade open was 47 and at close, it declined marginally to 46.25. While the trade was indeed profitable, a broader perspective may suggest that the system could potentially wait for a higher RSI for short entries, leveraging on the potential for an enhanced price reversal. A minor tweak like this, tested against our breadth of historical data, could improve the system’s predictive capabilities and profit efficiency.

We are progressively including an ensemble of machine learning models in our analysis. By utilizing deep neural networks (DNNs), our aim is to recognize complex nonlinear relationships between a diverse range of variables. For instance, future iterations of FundGPT may chart the correlation between CCI values and Bollinger Bands movements across multiple time frames to present more methodical trade opportunities. Recurrent Neural Networks (RNNs), particularly well-suited for time-series analyses, will allow FundGPT to account for previous periods’ market behaviors and compound it with present signals for improved precision.

Gradient Boosting Machines (GBMs), another arrow in our analytical quiver, could be useful in reducing the risk of losses. Reflect on the trade’s MACD signals that remained relatively stable across all timeframes. GBMs could be implemented to recognize patterns that may have otherwise included volatility as an additional risk parameter. This could decrease the possibility of entering trades amidst extreme market fluctuations while improving the accuracy and robustness of our algorithm.

Generative adversarial networks (GANs), capable of simulating vast synthetic financial data, add another dimension for possible improvement. Utilizing GANs, we could simulate outcome scenarios of trades that are on the fence of our entry parameters. For example, how much would our profit margin have been affected if the recent trade was initiated at 1.11330 rather than 1.11404? Such inferences can help us in designing self-evolving entry and exit strategies, increasing our trading algorithm’s effectiveness.

Moreover, a trade’s implications cannot be evaluated in isolation—it exists within a broader scope of market conditions. Comparing the EURUSD trade’s ATR of 0.00008 on the 1-minute chart with other currency pairs, indices, or commodities could reveal market-wide volatility trends. With this, FundGPT can adjust the risk tolerance of its algorithm, ensuring it remains synchronized with overall financial markets’ dynamics.

Training FundGPT also involves dealing with outlier trades that may skew our current model’s performance. Statistical techniques and machine learning models sophisticatedly fine-tune the impact of these trades, ensuring our model isn’t adversely biased by infrequent, high-impact events.

Our efforts toward refining FundGPT is an iterative process, continuously evolving with profitable trades and learning experiences alike. Backtesting on our vast repository of historical financial data, understanding intricate correlations often hidden from traditional analytical methods, and constantly tweaking our algorithms are at the heart of our journey toward a more sophisticated trading system. While we’ve made immense strides within this domain, the path of constant learning, adaptation, and improvement remains integral to our strategy. Leveraging advanced modern AI techniques is not only a possibility but our dedicated pursuit.

Disclaimer: This report is generated by an AI system using real data. While we strive for accuracy, there may be errors in interpretation. The information provided should not be solely relied upon for investment decisions. Trading, especially automated and experimental systems, carries a high level of risk. Invest responsibly and only with funds you can afford to lose.

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