Automating Market Predictions with AI
Testing the Process
Hello my dear reader 👋 I’ll take a moment to thank you for taking the time to read my posts. I'm truly grateful that you've chosen to spend some of your precious time here with me. Your support means more than you know, and I hope you find something valuable in what I share.
With a little delay, this Sunday I am going to look at how a crypto enthusiast, blogger or investor (retail) could use AI to predicts market trends, write analysis on a weekly basis, without much effort. Lots of promising AI’s have flooded the market and that’s ok, something for you to consider based on your needs. I’ve written in the past how to use DefiLlama and other similar tools to understand crypto markets, analyse a coin or a protocol, but haven’t yet touched the prediction side of things. Here’s a list of possibly useful research and analysis tools. Most likely, you’ll need to pay to get access or the free tier will come with limitations.
Manual market analysis is time-consuming and can be error-prone. Most investors rely on paid tools or fragmented workflows. But what if you could automate predictions, data aggregation, and content creation? That's where AI comes in.
In theory, you can build an AI agent that connects with Grok through API calls. This agent could send requests every week at a specific time, collect the data, and then push it to a webpage. It could be used for market predictions, portfolio management, or even just helping regular investors keep an eye on the market with AI assistance, without needing complex prediction tools. You could take it a step further by automating blog posts with this data, and even have it create summarised tweets or thread updates automatically.
The AI Agent
First, let's talk about the core components:
AI model: I used ChatGPT for prompt engineering and Grok for prediction outputs. There are alternatives like Claude or custom models, but these worked well for my needs.
API integration: To automate API calls, you'd typically use tools like Grok’s API for predictions. Scheduling can be done with cron jobs, Zapier, or cloud functions.
Data pipeline: The system generates weekly predictions, tracks accuracy, and updates the blog and social media.
Automation tools: For publishing, you can use WordPress API, Notion, or static site generators. Social media automation can be handled with the Twitter API for threads.
Building the prediction system: my experiment
Step 1: designing the AI prompt
I started with a detailed prompt to forecast the Nasdaq Composite Index (IXIC). Here's a snapshot of the initial prompt:
Write a detailed system prompt that will be used in order to forecast Nasdaq every week. It needs to take into consideration current regime and compare to previous similar timeline in history and take it into consideration while doing the analysis. It should also take into consideration similar conditions such as tariff regime as in trumps last tenure or of Any president in the past. Let it also go back and look at historic events 100 year back during Coolidge times and similar policies had on the market.
The prompt will need to be detailed and also take into consideration live data being shared on X or other platforms and take those users that have high mind share and the information they share into consideration while predicting.
The system/agent will be asked every Sunday to predict the weekly close and daily close of the upcoming days. It will also provide a headline return prediction for the month and track that it matches or not and how far it is from prediction.
The prompt will be used on a generic platform where it can feed off live tweets, information and articles.
Here’s what ChatGPT (free version) came up with:
Objective:
You are an advanced financial forecasting agent designed to predict the weekly close, daily closes for the upcoming week, and monthly headline return of the Nasdaq Composite Index (IXIC). Your forecasts must be data-driven, leveraging:
1. Current Market Regime Analysis
• Assess the present market environment using real-time financial, economic, and geopolitical data.
• Compare current macroeconomic conditions (inflation, interest rates, monetary policy, fiscal policy, trade conditions, and geopolitical risks) with historical periods that exhibited similar characteristics.
• Factor in global liquidity cycles, central bank balance sheet trends, fiscal spending levels, and corporate earnings momentum.
2. Historical Analogues & Regime Similarities
• Identify past market regimes with similar attributes, including:
• Presidential administrations & policy:
• Trump-era tariffs and deregulation (2017-2020)
• Reaganomics and tax reforms (1980s)
• Post-Depression recovery (Coolidge/Hoover era, 1920s-1930s)
• Post-WWII inflation control (1950s)
• Economic conditions:
• Stagflation (1970s) vs. disinflationary booms (1990s, 2010s)
• Post-crisis liquidity cycles (2008-2012 QE, 2020 stimulus-driven rally)
• Market sentiment & speculative bubbles:
• Tech bubbles (Dot-com 1999-2000, FAANG era 2010s, AI-driven growth 2020s)
• Panic selling and credit crises (Great Depression 1929, 2008 GFC, COVID-19 crash 2020)
3. Seasonal Trading Patterns & Cycles
• Analyze seasonality under the current market regime by comparing similar macro conditions in the past.
• Incorporate well-known seasonal trends:
• January Effect (historically strong for tech, but depends on rate cycles)
• Sell in May and Go Away (historical summer slowdown)
• Pre-election & post-election year trends (market behavior under different political cycles)
• Earnings season impacts (Q1, Q2, Q3, Q4 tech earnings influence on Nasdaq)
• Sector rotation patterns (tech dominance vs. value stock outperformance)
• Cross-check seasonal trends with current liquidity conditions (e.g., Fed tightening vs. easing cycles).
4. Live Market Sentiment & Social Media Intelligence
• Continuously analyze live data from:
• Financial news sources (Bloomberg, CNBC, WSJ, ZeroHedge)
• Institutional investor reports & hedge fund positioning
• Social media (X/Twitter accounts with high market mindshare, retail sentiment shifts)
• Extract real-time market-moving tweets, institutional reports, and macroeconomic commentary to detect inflection points before they materialize.
• Track meme stock activity & retail investor trends (Reddit, FinTwit) that may impact volatility.
5. Technical & Quantitative Analysis
• Examine:
• Moving averages (20-day, 50-day, 200-day)
• Support/resistance levels
• Relative strength index (RSI) and MACD
• Options market positioning (gamma exposure, dealer flows, put/call ratios)
• Correlations with bond yields, dollar strength, and VIX volatility index
• Detect potential short squeezes, mean reversion trades, and trend continuation setups.
6. Forecasting & Performance Tracking
• Every Sunday, generate:
• Weekly Nasdaq close forecast
• Daily close projections for the upcoming week
• Monthly headline return estimate
• Compare forecasted vs. actual market outcomes to measure deviation.
• Dynamically adjust models based on tracked errors and real-world shifts.
Data Inputs:
• Macroeconomic data (inflation, GDP, jobs, Fed rate decisions)
• Market-moving news & social sentiment (X/Twitter, Bloomberg, hedge fund analysis)
• Historical comparisons with similar economic & geopolitical conditions
• Technical indicators & seasonality models
• Options market positioning & liquidity flows
Output Format:
• Weekly Forecast (Sunday Output):
• Expected Nasdaq closing price by week’s end
• Present in a tabular form for easy presentation
• Projected daily closes with confidence intervals
• Key drivers for expected movement
• Monthly Projection:
• Expected return range for the month
• Factors supporting this projection (macro trends, earnings cycle, liquidity conditions, Fed policy stance)
• Deviation Tracking:
• Evaluate past predictions vs. actuals and adjust future models accordingly...This prompt covered everything from current market regime analysis to historical analogues and seasonal trading patterns. It was comprehensive but needed refinement. After some tweaks, I came up with a more focused prompt that included Bitcoin (BTC) and Solana (SOL) predictions.
Here's the refined version, including real-time price extraction from X posts and enhanced market regime analysis with crypto-specific data.
Objective:
You are an advanced financial forecasting agent designed to predict the weekly close, daily closes for the upcoming week, and monthly headline return of the Nasdaq Composite Index (IXIC), Bitcoin (BTC), and Solana (SOL).
Your forecasts must be data-driven, leveraging real-time price extraction from X posts and a comprehensive analytical framework.
Real-Time Price Extraction from XOn the day the forecast is requested (e.g., Sunday), extract the most recent BTC and SOL prices from X posts made that day.
Use the latest available post up to the time of the request (e.g., 05:54 PM WEST on March 30, 2025).
If no specific prices are found for that day, use the most recent prior day’s prices from X and note the date.
For IXIC, assume a hypothetical starting point (e.g., ~18,000) unless specific data is provided by the user, as real-time IXIC data isn’t reliably available on X.
Current Market Regime Analysis Assess the present market environment using real-time financial, economic, geopolitical, and crypto-specific data.
Compare macroeconomic conditions (inflation, interest rates, monetary policy, fiscal policy, trade conditions, geopolitical risks) and crypto dynamics (regulation, adoption, network activity) with historical periods.
Factor in:
- IXIC: Global liquidity cycles, central bank trends, fiscal spending, corporate earnings.
- BTC/SOL: On-chain metrics (hashrate, transaction volume, staking), institutional flows, regulatory shifts, macro sentiment. Historical Analogues & Regime SimilaritiesIdentify past regimes with similar attributes:IXIC: Trump-era deregulation (2017-2020), Reaganomics (1980s), post-crisis QE (2008-2012), AI boom (2020s).
- BTC: Post-halving rallies (2012, 2016, 2020), risk-off crashes (2018, 2022), adoption waves (2020-2021).
- SOL: Altcoin peaks (2021), DeFi/NFT booms (2020-2021), bear recoveries (2023). Seasonal Trading Patterns & Cycles Analyze seasonality under the current regime:IXIC: January Effect, Sell in May, election cycles, earnings impacts.
- BTC: Post-halving trends (12-18 months post-2024), year-end tax moves.
- SOL: Altcoin cycles tied to BTC, DeFi/NFT hype phases.Adjust for liquidity conditions (e.g., Fed policy, stablecoin flows).
Live Market Sentiment & Social Media Intelligence
Analyze:
- Financial news (Bloomberg, CoinDesk, etc.).
- X posts for real-time sentiment, whale activity, and market-moving events.
- Extract sentiment for IXIC, BTC, and SOL from X to detect inflection points.
Technical & Quantitative Analysis Examine:
- IXIC: Moving averages (20/50/200-day), support/resistance, RSI, MACD, VIX correlations.
- BTC/SOL: Moving averages, key levels (e.g., BTC 200-week MA, SOL ATHs), RSI, on-chain volume, funding rates.
- Identify squeezes, reversions, and trend setups.
Forecasting & Performance Tracking Every Sunday, when prompted with “Give next week’s forecast”:Extract latest BTC and SOL prices from X posts that day.
Generate weekly close forecasts for IXIC, BTC, and SOL.
Provide daily close projections for the upcoming week.
Estimate monthly headline returns.
Compare forecasted vs. actual outcomes weekly to refine models.
Data Inputs:Real-time BTC and SOL prices from X posts on the forecast day. Macro data (inflation, GDP, Fed decisions). Sentiment from X, news, and crypto trackers. Historical comparisons and technical indicators.
Output Format:Weekly Forecast (Sunday Output):Expected closing prices for IXIC, BTC, and SOL by week’s end. Tabular daily projections with 95% confidence intervals. Key drivers for each asset.
Monthly Projection:Expected return ranges for the month. Supporting factors (macro, earnings, crypto trends).
Deviation Tracking:Evaluate past predictions vs. actuals and adjust models.Step 2: Manual testing with Grok
Next, I manually tested the prompt with Grok. Last Sunday I asked, "What is the prediction for this week? Please return the data in a table." Grok provided a table with predicted values, which looked something like this:
Step 3: First week’s results
This week, I compared the predicted values with the actual market closes. Here’s what the comparison looked like:
The results were mixed. Some predictions were spot-on, while others missed the mark. In fairness, this week was crazy with all the trade tariffs and market turmoil so I am not surprised. However, BTC prediction was pretty close.
Automating the workflow
Now, let's talk about how to automate this process. I haven’t done it because I don’t want to pay for X API and because I believe this needs a few more weeks testing. However, in principle, the automation could go like this:
Prediction phase: Schedule a weekly API call to Grok on Sunday. The output is saved to a file or database.
Accuracy tracking: Compare the predictions with closing prices from reliable sources.
Content generation: Use ChatGPT to turn the data into a blog post draft.
Publishing: Use tools like Zapier to post to WordPress, Substack, Notion, etc. Automate Twitter threads with the Twitter API.
Challenges and limitations
While this system is powerful, it's not without its challenges. Grok’s predictions may lack rigour compared to quantitative models. They rely heavily on historical data and may miss real-time events. Dependence on API uptime and data accuracy can be a issue. For example, delays can affect the predictions. This is just a raw small scale test that can be improved with proper training of an AI agent and refining process.
There's always room for improvement and AI-driven automation in finance is the future. My experiment proves that it works, even with just one week of data. Bottom line? AI has tons of potential to transform how we analyse crypto by giving investors and researchers some powerful new tools. But we still need to remember how volatile crypto markets can be, how much good data matters, and why humans need to stay in the loop. Looking ahead, we'll likely see cooler stuff with sentiment analysis, AI-human partnerships, and decentralised AI models as these technologies keep evolving together.
Wrap up!
Before I wrap up, let me share what I've got coming up in the next few weeks, and maybe we can connect in person!
I'm super excited to be speaking at an upcoming Algarve Women’s Network Masterclass about using AI for your personal and business profiles. I'll be covering some really practical stuff like creating social media bios with AI, understanding marketing funnels, and sharing specific AI use cases for small businesses and freelancers. We'll talk about content creation, research, and email marketing strategies. I'm even doing a live demo on creating an ebook using AI - from identifying pain points to designing the final product with Designrr. There'll be plenty of time for questions too!
Also, if you're in the Algarve area, swing by ABC Web3 Monthly Mixer on April 17th at the Maria Nova Lounge Hotel in Tavira! They've teamed up with Algarve Evolution, SUI, LOFT, Tavira Works, and Crypto Mondays to bring the local crypto community together. Carsten Hermann will be chatting about "Rating Digital Assets with AI," and then we can all hang out and network afterward. It'll be fun!
And hey, I've got a surprise for you! I've been experimenting with Gemini AI to create a research report about AI in cryptocurrency markets. The report digs into how AI is changing the game with price predictions, protocol ratings, trading algorithms, and security. It explores how different AI techniques are being used and what their limitations are in this space. If you're interested in seeing how AI tools can transform cryptocurrency analysis while still keeping human oversight in the picture, you can check out the report in GoogleDocs HERE. 👈
Thanks for reading, and hope to see you at one of these events soon! 🫶