avatarHaben Joseph

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Abstract

market dynamics. This is where feature engineering becomes invaluable. By transforming and synthesizing the raw data using established technical indicators, we can provide the model with enriched insights that could explain underlying market patterns and trends. Such enriched data can significantly improve the model’s ability to anticipate future price movements.</p><p id="07ea">For this forecast, we will utilize a couple of technical indicators for our feature engineering like RSI, MACD, Bollinger Bands, Parabolic SAR, and Stochastic Oscillator. Additionally, we introduce lag features to capture temporal dependencies, ensuring our model benefits from both current and historical contexts.</p><p id="f61d"><b>Let’s calculate the features we will utilize in this forecast</b></p><div id="d4ed"><pre><span class="hljs-comment"># Compute RSI</span> df[<span class="hljs-string">'momentum_rsi'</span>] = RSIIndicator(close=df[<span class="hljs-string">'Close'</span>]).rsi()

<span class="hljs-comment"># Compute MACD</span> macd = MACD(close=df[<span class="hljs-string">'Close'</span>]) df[<span class="hljs-string">'trend_macd'</span>] = macd.macd() df[<span class="hljs-string">'trend_macd_signal'</span>] = macd.macd_signal() df[<span class="hljs-string">'trend_macd_diff'</span>] = macd.macd_diff()

<span class="hljs-comment"># Compute Bollinger Bands</span> bollinger = BollingerBands(close=df[<span class="hljs-string">'Close'</span>]) df[<span class="hljs-string">'volatility_bbm'</span>] = bollinger.bollinger_mavg() df[<span class="hljs-string">'volatility_bbl'</span>] = bollinger.bollinger_lband() df[<span class="hljs-string">'volatility_bbh'</span>] = bollinger.bollinger_hband()

<span class="hljs-comment"># Compute Parabolic SAR</span> psar = PSARIndicator(high=df[<span class="hljs-string">'High'</span>], low=df[<span class="hljs-string">'Low'</span>], close=df[<span class="hljs-string">'Close'</span>]) <span class="hljs-comment"># Assuming you have 'High' and 'Low' columns in your df</span> df[<span class="hljs-string">'trend_psar'</span>] = psar.psar()

<span class="hljs-comment"># Compute Stochastic Oscillator</span> stochastic = StochasticOscillator(high=df[<span class="hljs-string">'High'</span>], low=df[<span class="hljs-string">'Low'</span>], close=df[<span class="hljs-string">'Close'</span>]) <span class="hljs-comment"># Assuming you have 'High' and 'Low' columns</span> df[<span class="hljs-string">'momentum_stoch'</span>] = stochastic.stoch() df[<span class="hljs-string">'momentum_stoch_signal'</span>] = stochastic.stoch_signal()

<span class="hljs-comment"># Create Lag Features</span> df[<span class="hljs-string">'Close_Lag1'</span>] = df[<span class="hljs-string">'Close'</span>].shift(<span class="hljs-number">1</span>)

<span class="hljs-comment"># Drop NaN values introduced due to lag features and indicators</span> df = df.dropna()

<span class="hljs-comment"># Define features and target</span> X = df[[<span class="hljs-string">'momentum_rsi'</span>, <span class="hljs-string">'trend_macd'</span>, <span class="hljs-string">'trend_macd_signal'</span>, <span class="hljs-string">'trend_macd_diff'</span>, <span class="hljs-string">'volatility_bbm'</span>, <span class="hljs-string">'volatility_bbl'</span>, <span class="hljs-string">'volatility_bbh'</span>, <span class="hljs-string">'trend_psar'</span>, <span class="hljs-string">'momentum_stoch'</span>, <span class="hljs-string">'momentum_stoch_signal'</span>, <span class="hljs-string">'Close_Lag1'</span>]] y = df[<span class="hljs-string">'Close'</span>]</pre></div><p id="712f">The above code is organizing the dataset <code>df</code> into input features and a target variable for our model. The input features, captured under <code>X</code>, consist of various the features we calculated on and previously defined. The target variable, denoted by <code>y</code>, is the <code>Close</code> column, representing the daily closing price of EUR/USD, which our model aims to predict based on the provided features.</p><p id="bdfb"><b>Model Initialization and Training</b></p><div id="a7d2"><pre><span class="hljs-comment"># Initialize the model</span> model = xgb.XGBRegressor( learning_rate=<span class="hljs-number">0.75</span>, n_estimators=<span class="hljs-number">200</span>, max_depth=<span class="hljs-number">5</span>, subsample=<span class="hljs-number">0.9</span>, colsample_bytree=<span class="hljs-number">0.8</span>, colsample_bylevel=<span class="hljs-number">0.8</span>, gamma=<span class="hljs-number">0</span>, min_child_weight=<span class="hljs-number">1</span> )

<span class="hljs-comment"># Train the model</span> model.fit(X_train, y_train)</pre></div><p id="7ce9">Continuing from the previously discussed data preparation, this section of code dives into the model initialization and training phases using XGBoost. The <code>xgb.XGBRegressor()</code> initializes a regression model with specified hyperparameters to optimize the forecast. Key parameters include a learning rate of <code>0.75</code>, which determines the step size at each iteration while optimizing, <code>200</code> estimators or trees, and a maximum depth of <code>5</code> for each tree, among others. These hyperparameters play a role in controlling the model’s complexity and fit to the data.</p><p id="4e91">After initializing, the model is trained on the <code>X_train</code> and <code>y_train</code> datasets using the <code>fit</code> method. This step allows the model to learn the underlying patterns from the training data, preparing it to make future predictions on unseen data.</p><p id="95cf"><b>Performance Evaluation and Testing</b></p><div id="a823"><pre><span class="hljs-comment"># Predict on the test set</span> y_pred = model.predict(X_test)

<span class="hljs-comment"># Calculate performance metrics</span> mae = mean_absolute_error(y_test, y_pred) mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse)

<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Mean Absolute Error: <span class="hljs-subst">{mae}</span>"</span>) <span class="hljs-built_in">print</span>(<span class="hljs-string">f"Mean Squared Error: <span class="hljs-subst">{mse}</span>"</span>) <span class="hljs-built_in">print</span>(<span class="hljs-string">f"Root Mean Squared Error: <span class="hljs-subst">{rmse}</span>"</span>)

y_train_pred = model.predict(X_train)</pre></div><p id="8c56">After training the

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model on the historical data we evaluate its performance on unseen or test data. Using the <code>predict</code> method of the trained model, predictions (<code>y_pred</code>) are generated for the test dataset <code>X_test</code>. Subsequently, to assess the accuracy and reliability of these predictions, various performance metrics are computed:</p><ul><li><b>The Mean Absolute Error (MAE)</b> provides an average magnitude of errors between predicted and actual values.</li><li><b>The Mean Squared Error (MSE) </b>squares these errors to emphasize larger discrepancies.</li><li><b>Root Mean Squared Error (RMSE) </b>is the square root of MSE, providing error in the same units as the original data.</li></ul><p id="c92b">These metrics are then printed for clear visibility. We concludes by also predicting on the training set (<code>X_train</code>) with <code>y_train_pred</code>, to further analyze and compare the model’s performance on both training and test datasets.</p><p id="44c0">The following output displays the performance metricswhich assess the accuracy of our model’s predictions:</p><div id="6743"><pre><span class="hljs-attribute">Mean</span> Absolute Error: <span class="hljs-number">0</span>.<span class="hljs-number">009141215039947168</span> <span class="hljs-attribute">Mean</span> Squared Error: <span class="hljs-number">0</span>.<span class="hljs-number">000303615460154008</span> <span class="hljs-attribute">Root</span> Mean Squared Error: <span class="hljs-number">0</span>.<span class="hljs-number">017424564848340058</span></pre></div><ul><li><b>Mean Absolute Error (MAE): </b>At 0.0091, it shows the model’s average absolute deviation from the actual values.</li><li><b>Mean Squared Error (MSE):</b> With a value of 0.0003036, it indicates the average squared error, emphasizing larger mistakes.</li><li><b>Root Mean Squared Error (RMSE):</b> At 0.0174, it provides the average error in the original unit, illustrating the typical magnitude of error.</li></ul><p id="7500">The relatively low values across these metrics suggest that the model has a good degree of accuracy in its predictions. The model appears to be reliably forecasting the target variable, depicted with minimal deviations in the forecasted data when compared to the actual data.</p><p id="be3b"><b>Data Visualization</b></p><div id="2b49"><pre><span class="hljs-comment"># Create a new DataFrame for visualization</span> viz_df = pd.DataFrame({<span class="hljs-string">'True'</span>: y_test, <span class="hljs-string">'Predicted'</span>: y_pred})

<span class="hljs-comment"># Concatenate the training data for a complete view</span> viz_df_train = pd.DataFrame({<span class="hljs-string">'True'</span>: y_train, <span class="hljs-string">'Predicted'</span>: y_train_pred}) viz_df = pd.concat([viz_df_train, viz_df])

<span class="hljs-comment"># Plot the results</span> plt.figure(figsize=(<span class="hljs-number">14</span>, <span class="hljs-number">7</span>)) plt.plot(viz_df[<span class="hljs-string">'True'</span>], label=<span class="hljs-string">'True'</span>, color=<span class="hljs-string">'blue'</span>) plt.plot(viz_df[<span class="hljs-string">'Predicted'</span>], label=<span class="hljs-string">'Predicted'</span>, color=<span class="hljs-string">'red'</span>, alpha=<span class="hljs-number">0.7</span>) plt.title(<span class="hljs-string">'EUR/USD Forecast: True vs Predicted'</span>) plt.legend() plt.grid(<span class="hljs-literal">True</span>) plt.show()</pre></div><figure id="ce5c"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*cLlTTCFRMZqUBQIUBeya0g.png"><figcaption></figcaption></figure><p id="62e5">The visual representation of the EUR/USD currency pair’s forecasted versus actual values offers an insightful glimpse into the model’s capabilities. The close alignment between the blue <code>True</code> line and the red <code>Predicted</code> line for most of the chart affirms the model’s strong predictive proficiency, especially given the low Mean Absolute Error (MAE) of 0.0091. The few areas where deviations occur resonate with the Root Mean Squared Error (RMSE) of 0.0174, indicating the average magnitude of error.</p><p id="ba85">Notably, the small segment towards the right end, where predictions seem to diverge slightly, underscores the challenges of exact currency forecasting. Nevertheless, the model, as depicted in the graph and corroborated by the performance metrics, has shown remarkable accuracy in capturing the nuances of the EUR/USD exchange rate’s movements.</p><h1 id="fe27">Conclusion</h1><p id="3036">In conclusion, this exploration into Forex forecasting has underscored the critical interplay between data preprocessing, feature engineering, and model selection. Through this model we found that XGBoost in predicting the EUR/USD currency pair stands out, demonstrating the algorithm’s robustness and adaptability. Finally, the precision showcased by our model reinforces XGBoost’s reputation as an efficient tool to forecast financial data.</p><p id="0141">Read more of my stories here:</p><div id="c60c" class="link-block"> <a href="https://algocraft.xyz/eur-usd-forecasting-simplified-an-lstm-users-guide-337ccdda6158"> <div> <div> <h2>EUR/USD Forecasting Simplified: an LSTM User’s Guide</h2> <div><h3>LSTM, or Long Short-Term Memory, is a specialized type of Recurrent Neural Network (RNN) designed to recognize patterns…</h3></div> <div><p>algocraft.xyz</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*EUIE-cUkt3x2AqBX5nUotA.jpeg)"></div> </div> </div> </a> </div><div id="a6a6" class="link-block"> <a href="https://algocraft.xyz/how-to-get-131-return-with-mean-reversion-trading-strategy-from-stock-selection-to-backtesting-c623870adf31"> <div> <div> <h2>How to Get a 131% Return with Mean Reversion Trading Strategy: From Stock Selection to Backtesting</h2> <div><h3>undefined</h3></div> <div><p>undefined</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*o95y-D4ETf1Geqx3)"></div> </div> </div> </a> </div></article></body>

Will Novel AI Replace Your Job? ChatGPT Gives Some Advice

A chat on how to prepare for an AI takeover

Photo by Alex wong on Unsplash

Last night, I stumbled upon an Instagram post featuring a novel AI machine that can extract teeth — looked like it. It felt like something straight out of a sci-fi movie.

The patient wasn’t a human model. But no human was in sight.

As I pondered, I realized how automation could potentially replace repetitive manual labour in the future. However, for a machine to takeover dental training — a profession that can take 4 to 8 years depending on location — almost overnight felt humiliating.

Yes, maybe these novel AI technologies have the capacity to complement jobs and not necessarily replace them — at least now for the foreseeable future.

Yet, I couldn’t help but spiral for a moment.

Disclaimer: A little Google search showed that those technologies are in the research phase. And what I thought was extraction was probably not, rather more of screening.

I was concerned about what the future of work might hold, so I turned to our new virtual friend — ChatGPT — for some insights.

Here is the prompt I used…

This is a question on the future of work.
If you can list 10 ways and be as descriptive 
as possible, how do you think the human mind 
has to shift in order to survive the future 
world, given the AI-powered development and 
novel AI technologies? If I want to earn a 
great living with a stable job or perhaps an 
evolving job, yet manage to provide a stable 
income for my family and me, what are 10 
possible ways that you think our mentality 
has to shift?

I’ve listed below the response that ChatGPT gave me.

1. Embrace Lifelong Learning

With the rapid pace of technological development, it’s important to develop a mindset of continuous learning. This means staying up-to-date with emerging technologies, taking courses to develop new skills, and being open to learning new things.

2. Entrepreneurship

With the rise of AI and AI automation, entrepreneurship may become more important than ever. Starting your own business can provide greater job security and control over your income.

3. Emphasize Creativity and Innovation

The rise of AI automation will also place a premium on skills that can’t be automated, such as creativity, innovation, and problem-solving. Developing and honing these skills will be essential for success in the future.

4. Technological Literacy

As AI and AI automation continues to proliferate, it will be important to have a strong foundation in technology. This includes understanding basic programming concepts and staying up-to-date with emerging technologies.

With the increasing role of data in decision-making and automation, being able to understand and analyze data will be a valuable skill in many industries.

5. Cultivate Emotional Intelligence

As AI takes on more routine tasks, the importance of emotional intelligence will grow. Workers who can effectively communicate, collaborate, and empathize with others will be highly valued.

6. Adaptability

The future jobs market will require workers to be adaptable and flexible, as many jobs become obsolete. The ability to pivot and switch careers quickly will be essential to surviving in the rapidly evolving job market.

For instance, remote work has become increasingly popular since the pandemic, and it’s likely that it will become more widespread in the future. Embracing remote work and being comfortable with virtual collaboration will be essential for success.

7. Strong Communication Skills

Communication skills will become increasingly important as we move towards a more interconnected world. The ability to communicate effectively with people from diverse backgrounds will be key to success.

8. Emphasize Globalization

With the rise of the global economy, it’s important to develop a global perspective. This means being open to different cultures and ways of doing things and being able to communicate effectively with people from different parts of the world. Working effectively with people from diverse backgrounds will be essential

9. Balance Technology and Humanity

With the increasing role of AI in decision-making and automation, it’s important to consider the ethical and social implications of these technologies and work toward creating a sustainable and equitable future. Balancing the benefits of technology with the needs of humanity will be essential for creating a better future for all.

10. Foster Agility and Resilience

The future will be characterized by uncertainty, volatility, and disruption, so being agile and resilient will be key in navigating the challenges and opportunities.

The Takeaway Message;

Growing up, my dad has this thing he says quite often, “you learn how to type on a keyboard with keys facing up. Next thing you know, a new version of a keyboard will emerge with keys facing downward, and you’ll need to know how to work with that version too.”

His point is to survive; you need to embrace lifelong learning. He probably didn’t think this is how the world would turn up — at least not this early — but we continue to live in a time of rapid technological advancements and digital transformation.

It’s the age of AI, and whether AI takeover is something to worry about or not, to stay relevant, we must constantly adapt and learn new skills. Lifelong self-improvement is no longer a luxury but a necessity.

As we embrace new technologies and ways of working, we must also cultivate an agile and growth-oriented mindset that allows us to navigate change with ease and grace.

Future jobs will shift gradually. However, history has shown that human beings possess a remarkable ability to adapt and evolve. And I can only trust that will be the case here.

If you made it this far, thank you for reading.

This post was inspired by a mental whirlwind — and that Instagram post that I couldn’t seem to find after.

I then came up with a few prompts that I hope you, the reader, would be inspired to write about.

  • If you think this post is wrong — in any way possible, like ethics (e.g., that I relied on ChatGPT to answer my question) or anything else — why do you think it is wrong? In your opinion, where is the lie?
  • If you’re someone who has a profession in those areas (e.g., Tech or any other platform that is actively employing AI innovation or AI automation), how could you share your expertise and background to fill in the gap in our knowledge? What’s the part that we, the public, are not seeing?
  • For all the creatives out there, what do you have to say? Do you think emphasizing on “Creativity and Innovation,” as listed above, is the way to survive an AI takeover? Why do you think that is possible? Why do you not think that is possible?
  • I was a little surprised when I saw cultivating “Emotional intelligence” as one way to thrive with the advent of AI innovation. How do you think that could help with future jobs?

If you end up using these prompts to write an article, please tag me, as I would love to read your views.

Special shout out to Katy Ramm. She wrote a great article on how to use the most fitting keyword for your blog here.

Disclaimer: As mentioned above, this article has sections generated using artificial intelligence technology, so the content may not be 100% factual. Any reliance you place on such information is, therefore, strictly at your own risk.

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Future Of Work
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Emotional Intelligence
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