Questions score
- Min
- 2
- Median
- 10.0
- Max
- 14
- Q1
- 7.0
- Avg
- 10.1
- Q3
- 14.0
Stock Markets Analytics Zoomcamp 2025
Homework 1: Intro and Data Sources Statistics
Distribution of scores and reported study time for this homework.
Submissions
138
Median total score
10
Average total score
10
Score distribution
All values are points.
Learning in public score
- Min
- -
- Median
- 0.0
- Max
- 3
- Q1
- 0.0
- Avg
- 0.2
- Q3
- 0.0
Total score
- Min
- 2
- Median
- 10.0
- Max
- 17
- Q1
- 7.0
- Avg
- 10.3
- Q3
- 14.0
Time distribution
All values are hours reported by students.
Lectures
- Min
- 0.0
- Median
- 2.0
- Max
- 20.0
- Q1
- 1.8
- Avg
- 3.0
- Q3
- 3.0
Homework
- Min
- 2.0
- Median
- 5.0
- Max
- 40.0
- Q1
- 3.0
- Avg
- 7.2
- Q3
- 9.0
Question breakdown
Correctness and answer distribution per question.
1. Which year had the highest number of additions to the S&P 500 index?
129 / 138 correct (93.5%)
1
2017
129 (93.5%)
2
2019
0 (0.0%)
3
2008
5 (3.6%)
4
2023
4 (2.9%)
2. How many indexes have better year-to-date returns than the US S&P500 as of May 1, 2025?
114 / 138 correct (82.6%)
1
5
3 (2.2%)
2
7
11 (8.0%)
3
9
114 (82.6%)
4
8
9 (6.5%)
3. Median duration (in days) of significant market corrections in the S&P 500 index
91 / 138 correct (65.9%)
1
39
91 (65.9%)
2
113
6 (4.3%)
3
95
23 (16.7%)
4
74
12 (8.7%)
4. Median 2-day percentage change in AMZN stock price following positive surprise earnings days
62 / 138 correct (44.9%)
1
4.5
9 (6.5%)
2
3.2
15 (10.9%)
3
2.6
62 (44.9%)
4
1.8
43 (31.2%)
5. Idea for your capstone project
138 / 138 correct (100.0%)
| Answer | Count |
|---|---|
| my idea is to explore text in 10K file, and how filing related to next day stock price change. like hw1/q4, but not only EPS. initial idea is to see the business section, how certain word appearance related to stock price change. after the initial research, scale up to LLM model, using RAG to identify a sentiment score. | 1 |
| I am interested in both sentiment analysis of financial news and predicting stock market trends using historical market data. Therefore, I aim to combine these two areas by building predictive models that incorporate both historical stock prices and financial news sentiment, focusing on the US technology stock market. The goal is to develop a model that can support decision-making for individual investors over a 60-day investment horizon. I believe this approach can provide valuable insights and help retail investors make more informed and timely investment decisions. | 1 |
| Build a robust short-term trading prediction model to generate buy and sell signals for: - **US market:** S&P 500 index (using SPY or similar ETF) - **Indian market:** Xtrackers Nifty 50 Swap ETF Focus: Short-term horizon (5–30 days), aiming to capitalize on price momentum and market sentiment. | 1 |
| I want to simulate different "buy-the-dip" strategies—such as buying when the market drops by 5%, 10%, or 15%—using historical S&P 500 data. I plan to analyze and visualize the performance of these strategies over time. | 1 |
| Explore the use of machine learning model in malaysia stock market | 1 |
| Maybe I will look for expected earning date for all tech stocks and if the median return 2 days post EPS announcement is higher than 80% historical return of all tech stocks in the past 20 years I will buy it | 1 |
| a crypto day trading bot using Binance and a long-term stock portfolio strategy using GBM+ in the Mexican market. Both would follow a pipeline of data collection, modeling, and real execution. I aim to eventually apply them with real capital, though many of the metrics and techniques involved are things I hope to learn throughout the course. | 1 |
| I would like to build a news-driven sentiment analysis model to predict short-term stock price movements of large-cap US tech companies (e.g., AMZN, AAPL, MSFT, GOOG). My project will combine financial news headlines, social media mentions (especially from Reddit/StockTwits), and earnings reports to gauge market sentiment. The goal is to develop a model that predicts 2-5 day returns following major news events or earnings announcements. I plan to use a combination of natural language processing (NLP) for text sentiment analysis and technical indicators like RSI, MACD, and moving averages for momentum insights. I will train a classification or regression model to evaluate the potential price direction or percentage change. This project aligns with my background in media analytics and data engineering, and it would allow me to apply real-time data collection (via APIs and web scraping), feature engineering, and ML modeling in a finance context. | 1 |
| I want to build a RSU Investment Strategy Tracker for Tech Employees like myself. The project aims to help people —especially employees —make better decisions about when to sell their RSUs by using data from technical indicators, news, and insider trades. | 1 |
| I aim to modernize my personal investment tools by automating and industrializing the algorithmic analysis I currently perform with Excel and macros. My approach is not focused on classic predictive models (ML or deep learning) but rather on efficient data processing and advanced analysis, particularly studying correlations between portfolio lines. The goal is to build a robust technical component that can be integrated into a future SaaS, improving productivity and decision-making without revealing my proprietary methods. However, I wonder if such a project, centered on algorithmic optimization and data analysis without predictive modeling, would be accepted within the Zoomcamp framework, which seems more prediction-oriented. | 1 |
| I'm not entirely sure yet but I definitely want to build a model for the Canadian Stock Market, probably focused around tech. | 1 |
| Short-term stock trading strategy for the Sri Lankan stock market (CSE) focused on the most actively traded companies. | 1 |
| I want to explore different Stocks from USA market. Also, looking at historical data of different USA stocks for last 20-30 years, I want to build a good ML model that will predict which stock is likely to be profitable in 10years time. | 1 |
| Apply a series of momentum indicators to a basket of ETFs / single stocks across markets to design an investment strategy with weekly or monthly rebalancings (TBD depending on results). Potentially adjust overall market exposure based on macro factors. | 1 |
| Predict duration of stock market correction for macro indices. It would be interesting to create an interval showing the error of the prediction. | 1 |
| Volatility Prediction and Options Strategy: Develop a model to predict periods of increased market volatility and create an options strategy that profits from forecasting volatility | 1 |
| A comprehensive analysis of the European Central Bank’s monetary policy decisions and their impact on the performance and volatility of major European equity indices over the past decade. The project aims to quantify causal relationships and assess sectoral sensitivities using time-series econometric models and event study methodologies. | 1 |
| My goal is to predict long-term market movements (months to years ahead), using economic theory-based models to provide a structural foundation that complements or even outlasts pure data-driven or sentiment-based models | 1 |
| Develop a low-risk, quarterly-updated ML-based stock selection strategy focused on momentum and stability, targeting IT and Healthcare stocks from the US and Germany. | 1 |
| I would like to explore US ETFs market. In particular I am interested rotation between ETFs. If I can identify the rotation pattern, I can identify the current market phase and be more/less defensive with my investment. | 1 |
| i will create my own portfolio of 10 good performing reliable stock from top 10 highest growing economies of world | 1 |
| I want to make a short-to-medium term (30-90 days) forecast model that combines statistical model and ML prediction for the US and Hong Kong market large-caps (which I trade both), using tech and economic indicators and alternative data (maybe news). I would also like to consider risk-side functions like VaR and CVaR. | 1 |
| Short-Horizon FX Momentum in Emerging Markets | 1 |
| For my capstone project, I would like to develop a mid-term prediction model for equity markets in both Latin America (specifically Colombia, Brazil, Mexico,Chile, Peru, starting with colombia) and the United States. My focus will be on forecasting stock returns or index levels over (Somethign related with energy or tech) a six-month investment horizon. I am interested in combining traditional technical indicators with macroeconomic variables, particularly each country’s data to improve predictive power. | 1 |
| At this stage, I have not made a decision yet. A possible option is to generate a large set of features based on technical analysis indicators, as well as simple features characterizing time series. And then build an ML predictive model for the classification task predicting market behavior N trading periods ahead. Most likely, such a model will be able to work on short-term time periods (minutes) on the forex market, on cryptocurrencies, on stock futures. But it is unknown whether the exchange commission will be covered. It is also possible that such a model could work on daily periods in the stock market, where there are long-term trends. Perhaps, in this case, adding macroeconomic and fundamental data will be useful. It may also be worthwhile to first analyze the correlations of prices of various trading assets with macroeconomic and fundamental data. Perhaps, some of them will show greater correlation, and greater potential for use in such trading strategies. | 1 |
| : I want to build a short-term prediction model focusing on IPO trends over a 3 or 4 month period and including news coverage as a predicting variable, based on which I can make a trading decision (buy/sell IPO stocks) in Germany and Singapore markets. | 1 |
| Client Portfolio Recommender for a Bank’s Wealth Division. Develop a Python-based tool that supports a bank’s wealth management division in recommending personalized investment portfolios to clients based on their risk profile and current market data. | 1 |
| I would like to build an Entry/Exit Signal Classifier Based on Technical Patterns because, I want to learn algorithmic trading | 1 |
| Analyzing the Correlation Between the Stock Market and the Cryptocurrency Market | 1 |
| I wish to work on fundamental analysis, Still need to search about the organization. | 1 |
| I would like to build a machine learning model that detects early warning signals of significant market corrections in the US stock market, using historical data from the S&P 500 index. The model will focus on identifying conditions that typically precede drawdowns larger than 5%. I plan to incorporate technical indicators (e.g., RSI, MACD, Bollinger Bands), macroeconomic indicators (e.g., interest rates, inflation trends), and historical drawdown patterns to train a classifier that flags periods of elevated correction risk. The goal is to develop a signal-generation system that helps long-term investors reduce exposure before downturns. I am particularly interested in time-series modeling approaches like rolling windows, XGBoost, and possibly LSTM if time allows. | 1 |
| I want to build a personal investment assistant that recommends U.S. stocks to retail investors based on a combination of fundamental strength, price momentum, and recent investor sentiment. The assistant will use machine learning to predict the likelihood that a given stock will outperform its sector over the next 30 trading days — a horizon relevant to active retail traders. | 1 |
| I plan to build a short-term predictive model for the US technology sector stocks, specifically targeting Amazon (AMZN), Apple (AAPL), and Microsoft (MSFT). The model will focus on forecasting 7-day forward returns immediately following earnings announcements. Inputs will include quantitative features such as historical price data, technical indicators (RSI, MACD, Bollinger Bands), and earnings surprise metrics (percentage surprise, EPS actual vs estimate). In addition, I will incorporate sentiment analysis derived from financial news articles and Twitter data within a 3-day window around earnings dates to capture market sentiment shifts. The model architecture will explore gradient boosting methods (e.g., XGBoost) and transformer-based natural language processing for sentiment features. Furthermore, I intend to add a market regime detection component using volatility and macroeconomic indicators to adapt predictions during bull versus bear markets. This project leverages my background in machine learning and finance, and aims to provide an actionable signal for swing trading strategies centered on earnings-driven volatility in large tech stocks. | 1 |
| I'm building EarningsAI, an automated pipeline that transforms earnings calls into real-time intelligence. The system continuously monitors financial YouTube channels for new earnings call videos. When detected, it instantly extracts and cleans transcripts, identifies the company, and cross-references statements with official SEC filings. Using multi-stage AI analysis, it identifies key financial metrics, guidance changes, sentiment shifts, and flags discrepancies between management statements and SEC data. The entire process runs in under 10 minutes, generating alerts for material events like revenue surprises or competitive threats before manual analysts finish taking notes. The core technical innovation lies in the dual-source validation—automatically verifying YouTube content against SEC filings—which creates a significant timing advantage for financial decision-making. | 1 |
| I want to build a short-term prediction model for the technology sector in Japan and the USA, focusing on the largest tech companies over a 30-day investment horizon. I plan to use RSI and MACD technical indicators combined with sentiment analysis from English-language news coverage to generate predictions. | 1 |
| Evaluating the Effectiveness of Diversification Strategies During Market Crises | 1 |
| For my capstone project, I would like to build a machine learning model that predicts the short-term (1–30 day) price movement of companies listed in US small-cap indices (e.g., Russell 2000). The goal is to identify patterns or signals that can be used for tactical trading or portfolio rotation strategies. | 1 |
| "Adaptive Pairs Trading with Machine Learning Regime Detection" Objective: Build an intelligent pairs trading system that dynamically adapts its parameters based on market regime detection using machine learning. Asset Class & Market Focus: US equity market, focusing on ETF pairs and large-cap stock pairs Primary focus on sector ETFs (XLF/XLI, XLE/XLU, QQQ/IWM, etc.) Secondary focus on individual stock pairs within the same industry Investment Strategy: Inspired by Chan's discussion of cointegration and Conditional Parameter Optimization (CPO), I want to create a pairs trading system that: Identifies cointegrated pairs using statistical tests (Augmented Dickey-Fuller) Detects market regimes using machine learning (Random Forest/XGBoost) based on: VIX levels and changes Interest rate environment Market breadth indicators Sector rotation patterns Adapts trading parameters dynamically based on detected regime: Entry/exit thresholds (z-score levels) Position sizing Stop-loss levels Mean reversion half-life expectations Technical Approach: Time Horizon: 1-30 day holding periods with daily rebalancing Features for ML Model: Technical indicators: RSI, MACD, Bollinger Bands Market microstructure: Bid-ask spreads, volume patterns Volatility measures: Historical volatility, VIX term structure Fundamental ratios: P/E ratio spreads, sector momentum Prediction Target: Optimal z-score thresholds for entry/exit Expected Outcome: A system that outperforms static pairs trading by adapting to changing market conditions, similar to Chan's CPO methodology but applied to pairs trading specifically. | 1 |
| Local recommender for finding well perfoming dividend stocks using local LLM and external MCP server | 1 |
| would like to develop a machine learning model to detect early warning signals of retail investor herding behaviour in the German equity markets, focusing on highly volatile technology and consumer discretionary stocks. The goal is to forecast short-term (7-day) abnormal returns or volatility bursts following coordinated buying/selling behaviour. | 1 |
| I want to build a short-term prediction model for the US stock markets, focusing on the largest stocks over a 30-day investment horizon. I plan to use RSI to generate predictions. | 1 |
| For my capstone project, I am particularly interested in building a trading agent for Indonesian bank stocks and the IHSG. To support this, I explored several additional metrics and time series data that could enhance the agent’s decision-making process. Besides the usual OHLCV price data, I included technical indicators such as RSI, Bollinger Bands, and moving averages to help the agent identify entry and exit signals based on trend and momentum. I also considered incorporating volatility measures, like the VIX (or local alternatives), and put/call ratio data to allow the agent to adapt its strategy during periods of high uncertainty or extreme market sentiment. Additionally, macroeconomic indicators—including interest rates, USD/IDR exchange rates, and inflation—are highly relevant for bank stocks, so my agent would use these to adjust risk exposure. To enrich the agent’s context-awareness, I planned to use sentiment data derived from news headlines or social media, processed via APIs or simple NLP models, to capture market mood shifts. For risk management and portfolio diversification, correlation analysis between stocks and macro factors was also explored. I retrieved this data using Python libraries such as yfinance (for price, volume, and technical indicators), pandas (for data processing and correlation), and external APIs for news and macroeconomic data. By combining these diverse data streams, the trading agent can make more informed, adaptive decisions in real-time, better capturing opportunities and managing risks in the Indonesian market. | 1 |
| compare sp500 with criptocurrencies | 1 |
| I want to build a predictive model that complements my analog (discretionary) market analysis. My initial goal is to forecast stock prices on a week-by-week basis and visualize the performance of the model over time. The idea is to integrate this model into my trading or investment workflow as a systematic layer that supports my manual decisions. In the future, I plan to develop a more complex model that incorporates both macroeconomic and microeconomic calendars (e.g., economic releases, central bank meetings, earnings dates). This would allow the model to better anticipate volatility and directional moves around scheduled events, and potentially simulate different market regimes. I aim to start with simple price and volume-based features, then expand to more structured and calendar-aware inputs. | 1 |
| my idea is to build a short term prediction model focusing on the correlation between major index (e.g. S&P 500) and some major crypto such as BTC. Something like to use weekend crypto trading data to predict the stock market performance in the upcoming week. "To make a Monday bet" so to speak. | 1 |
| Uma possível ideia para o projeto final: Desenvolver um pipeline que baixa diariamente: OHLCV de ETFs setoriais do S&P 500 via yfinance Fatores macro (yield curve 10y-2y e ISM PMI) via FRED Fundamentos forward P/E via Alpha Vantage Ele agrega tudo em Parquet, gera features em pandas, treina um modelo de classificação walk-forward que prevê qual setor superará o S&P 500 no mês seguinte e, a cada pregão, publica um sinal de alocação nos 3 melhores setores. O fluxo é orquestrado por Airflow, salva resultados numa tabela SQLite/Parquet e envia o relatório diário (retornos + drawdown) por e-mail. | 1 |
| ML-driven approach to derive investment strategy that over 1-year horizon can generate gains in the Europe/America stock markets. | 1 |
| For my capstone project, I plan to develop a rule-based algorithmic trading strategy that combines positive earnings surprises with technical indicators such as the 21-day EMA, Keltner Channels, RSI, and MACD to identify short-term trading opportunities in U.S. equities. The strategy will be backtested using Python to evaluate performance across various market conditions with integrated risk management rules. | 1 |
| Pairs Trading with Cointegrated Assets, Identify a pair of stocks that are historically cointegrated (their prices tend to move together). Trade on temporary deviations from their long-term relationship | 1 |
| For my capstone project, I propose developing a machine learning-based trading strategy for mid-cap technology stocks in the U.S. market, focusing on a 90-day investment horizon. The goal is to predict stock price movements and generate buy/sell signals to outperform a benchmark index, such as the S&P MidCap 400 Technology Index. This project aligns with my interest in technology-driven growth companies, my aspiration to apply machine learning in quantitative finance, and my prior knowledge of data analysis and stock market dynamics from working on projects like the AMZN earnings surprise analysis. | 1 |
| I would like to analyse the stock performance between Nvidia and AMD, given their similarity in company business. As Nvidia stocks are currently expensive, I wonder if AMD has the potential to grow like Nvidia and hence it's worth buying at its current price? | 1 |
| I want to build some sort of prediction model for stocks, mainly in the US, that I find interesting. Eventually, I want to apply this as a trading strategy and see if my knowlegde and models will pay off. | 1 |
| I would like to work with european stock market as I earn in € and if I focuss on other market I would need to asume additional comissions and the risk of exchange rate. On the other hand, I work with IA and know better Tech Market so I cannot totally reject at this point the idea of focussing or NASDAQ. | 1 |
| Analyze the forex market with focus on specific currency pair, build a predictive model to predict the market bias for those pairs and create an automated trading system for trading the pairs. | 1 |
| Build a predictive model for short-term price movements of UK renewable energy stocks by combining time-series financial data with news sentiment analysis. | 1 |
| Deep Reinforcement Learning for Multi-Asset Crypto Portfolio Optimization and Market Regime Detection | 1 |
| I want to explore how key political events (General elections, Brexit, etc) impacted UK market volatility. | 1 |
| Since we're approaching an inflation target of 2% (FED) and the market expects rates to be cut soon (macro-pivot), I believe commodities are very promising asset class. What is more, USA needs weak dollar to stay competitive and this also favours commodities (inversely correlated). Taking into account significant demand for energy from Data&AI sector, I would like to focus on energy commodities. The idea is to outperform SP500 index by choosing only the companies from energy sector (XLE). | 1 |
| I'd like to create a short-term prediction model for the US or Danish energy trading market. | 1 |
| I want to build a stock market classification ML model that will recommend stocks to buy from a given list. I plan to compare its performance against a dummy model based on moving averages. I plan to use the F1-score metric. | 1 |
| I want to develop strategy that will predict Indian stocks growth on daily/weekly/monthly timeframe using technical indicators and patterns. | 1 |
| I want to build a short-term prediction model for US Market particularly Nasdaq, HK Market,Malaysia Market and maybe crypto and commodity using RSI and support and resistance | 1 |
| Time series analysis of stock (using Amazon dataset) | 1 |
| Explore crypto coins included in the reserve | 1 |
| No idea yet, design in progress | 1 |
| not yet | 1 |
| One of two things. 1) Machine learning algorithm to improve the edge of 'overnight drift'. 2) Algorithm to dynamically weight my DCA investments. | 1 |
| Build a “Show-to-Stock Signal” platform that monitors streaming episodes and films, quantifies on-screen brand exposure (logo appearances, verbal mentions), and blends that information with real-time viewership and social-media buzz. Using these inputs, the system: 1. nowcasts short-term revenue impact for the publicly listed brands, 2. predicts the stock’s likelihood of outperforming in the following week. Outputs are delivered through an API or dashboard, giving equity investors an informational lead of several days to weeks over traditional sell-side estimates. | 1 |
| I want to build a short-term stock price prediction model using time series data for high-volume U.S. equities. The goal is to forecast next-day or 3-day returns using lagged price data, volatility, volume, and technical indicators like RSI, MACD, and moving averages. The model will focus on identifying short-term directional moves and be tested on large-cap stocks like AMZN, AAPL, and MSFT. | 1 |
| Regime-Aware Volatility-Carry Strategy for U.S. Equities. Aim"Build a machine-learning model that classifies the current S&P 500 market regime (quiet up-trend, volatile up-trend, quiet down-trend, volatile down-trend) and allocates between long equity, long VIX futures, and cash to outperform buy-and-hold on a risk-adjusted basis. Aim: Build a machine-learning model that classifies the current S&P 500 market regime (quiet up-trend, volatile up-trend, quiet down-trend, volatile down-trend) and allocates between long equity, long VIX futures, and cash to outperform buy-and-hold on a risk-adjusted basis. | 1 |
| I want to build a short-term prediction model for palantir and bitcoin and find out the long-term buying and selling opportunity singals | 1 |
| Developing a Relative Strength-Based Stock Selection Strategy Using SPY as a Benchmark. The goal of this capstone is to build a machine learning-powered stock trading strategy that identifies stocks showing consistent relative strength or weakness compared to the SPY index, and uses that information to generate long/short signals. This strategy will focus on: Ranking individual stocks based on their relative performance vs. SPY Identifying breakout or reversal opportunities using RS signals and ML classification | 1 |
| Predict growth trends of publicly traded 3D printing companies using macroeconomic indicators, and identify market shifts to guide investment strategies. Description: This project applies time series forecasting models to analyze the growth of leading 3D printing industry stocks across major regions (US, Western Europe, Asia). The models are trained using historical stock prices, alongside macroeconomic variables such as GDP growth, industrial production indices, and manufacturing R&D spending. The aim is to: Identify macroeconomic signals preceding major shifts in the 3D printing sector Compare regional trends in industry growth Evaluate the timing and effectiveness of selected investment strategies (e.g. momentum vs. value) in response to these signals This project combines financial forecasting with economic insight to support strategic long-term investments in the additive manufacturing sector. | 1 |
| I plan to use momentum based indicators to build trend following strategies and automate intraday trading, I already have something in the works and am testing various compoments and parameters including MACD, RSI, Volume, VWAP, ATR, CCI and some others. I plan to use RandomForestClassifier to predict buy, sell, no_action categories. | 1 |
| I would like to predict the growth of environmental consulting sector using macroeconomic indicators and compare trends across selected regions (US, Western Europe, Eastern Europe, Asia). To do that I plan to build time series models and train them using stock prices, GDP, and environmental investment indices from the last 10-15 years of data. After that I will use the estimations to forecast the growth of public environmental consulting companies, compare the forecasts across regions and evaluate cross-country investment potentials in the sector. | 1 |
| My current investment strategy focuses on index funds, but I'm eager to use data-driven methods to diversify further. I'm particularly interested in applying machine learning to identify promising asset classes, country-specific ETFs, or industry verticals that could complement my existing portfolio. The initial insights from this course have already shown me the potential of such an approach. For my capstone, I plan to develop a strategy that focuses on the US market, specifically sector-specific ETFs and potentially large-cap growth stocks.My goal is to build a model that can identify opportunities to tactically diversify, ultimately aiming for a more robust portfolio than a purely passive index strategy. | 1 |
| I want to build a machine learning model to estimate potential entry and exit points in the crypto market based on historical data and technical indicators. | 1 |
| "I am thinking about creating an algorithm for trading commodities over a two-week horizon. I plan to use RSI aand MACD technical indicators for tracking the price movement and Bolinger bands for tracking the volatility" | 1 |
| I would like to build a short-term prediction model for cryptocurrency price movements, focusing on major crypto assets like Bitcoin (BTC), Ethereum (ETH), and Solana (SOL) over a 30-day investment horizon. The goal is to forecast directional moves (up/down) or percentage returns on a monthly basis. I plan to combine technical indicators (like RSI, MACD, moving averages) with on-chain metrics (e.g., wallet activity, exchange flows) and possibly social sentiment (from Twitter or Reddit) to capture both trading patterns and market psychology. The final model could be used to generate signals for a simple strategy (e.g., long/short or hold/cash). I’m interested in understanding how different types of features contribute to predictive performance in highly volatile and sentiment-driven markets like crypto. | 1 |
| Greek market forecasts | 1 |
| am thinking of a solo stock predictor using daily data to predict for next 10 weeks | 1 |
| I want to build a short-term and long-term prediction model for selected ETF, focusing on weekly and monthly horizon. I plan to use EMA, SMA and Stochastics technical indicators. I would like to use news coverage data and due to the ETF composition, try to correlate with other industries or news not directly related to generate predictions. I also would like to create a model for day trading based on volume analysis and change of prices in very short term. | 1 |
| I want to build a portfolio of stocks that could see short term uptick with tariffs using news coverage and other metrics. | 1 |
| Analyse East African Stock markets | 1 |
| I will work on making a Stock Price Prediction Model using either Deep Learning or LSTM or something, or I will make a Quantitative Momentum Investing Strategy Model. | 1 |
| I started investing many years ago, and at some point began trading US ETFs and lastly moved into US Options. I'm off markets for a while now, and my main goal is to restart with European and eventually US ETFs, medium to long-term, possibly hedging some risk, as well seize short-term strategies, with Options. For this I need to ponder sentiment, cambial risk (forex?), and the market moves, devising a prediction system to select my trades. If this is feasible, and how much, I don't know. I need to progress on this zoomcamp to better refine and detail my definition! | 1 |
| I think it is an interesting time to look at the Aerospace and Defense sector, but my conscience wouldn't allow me to profit from it. Instead I would like to build a model for the engineering and construction industry focusing on European stock markets. I need to get a better understanding of the industry and different types of stock market analysis to be more specific. | 1 |
| develop a portafolio manager to help investment desicions | 1 |
| Magnificent 7 (minus Telsa) intra-day trading using moving averages and RSI; may includ candlestick analysis when generating signals. | 1 |
| Build a signal model for tardings correlated indexes and emerging energies and commodities markets. | 1 |
| Build a model to predict if S&P 500 stocks will go up or down in the 5 days after positive earnings surprises, then create a basic trading strategy. | 1 |
| I want to build a long-term prediction model and analyze the historic performance for the US/India stock markets, focusing on the start-ups over a 2-year investment horizon. I plan to use RSI and MACD technical indicators and news coverage data, sentiment analysis and time-series analysis to generate predictions. | 1 |
| I want to build a short-term prediction model for the Australia stock markets, focusing on the largest stocks over a 90-day investment horizon. | 1 |
| I want to build a short-term machine learning prediction model focused on the top-traded stocks in the Mexican Stock Exchange (BMV). The model will aim to predict 2-day to 5-day returns following key market events, specifically earnings reports and abnormal price moves. | 1 |
| I want to build a short-term forecasting model for monthly inflation in Argentina, using a combination of macroeconomic, monetary, and real-time market indicators. I plan to focus on a 1-month prediction horizon. Input variables will include monetary aggregates (M2), official and parallel USD/ARS exchange rates, central bank interest rates, commodity prices (soy, oil, wheat), and global inflation trends. The goal is to capture the key drivers of inflation volatility in Argentina and provide early warning signals. I will use tree-based models (e.g., XGBoost) and recurrent neural networks (LSTM) to evaluate performance. | 1 |
| Something related with ML, like asset allocation model that dynamically adjusts portfolio weights across asset classes (equities, bonds, and currencies) in emerging markets based on macroeconomic indicators and sentiment analysis from news and social media. | 1 |
| Focus on a correlation between stocks with companies that use imported parts and the countries the parts are imported from. | 1 |
| Build an algorithmic trading strategy that predicts sector rotation patterns in the US stock market using economic indicators and basic sentiment analysis from freely available news data. | 1 |
| Build a stock recommendation system based on risk tolerance, income, area of investment etc. | 1 |
| i'd like to create asset portfolio for investing with balanced risks and predict future value | 1 |
| planning to build a prediction model on individual big tech stocks, benchmarking against NASDAQ index to select the better strategy, in the period of 3months. Hopefully can leverage some media release of individual stocks | 1 |
| I’m interested in building a capstone around two complementary investment strategies focused on Russia: one for long‐term, retirement‐style holdings in large, stable Russian companies and another for shorter‐term, tactical trades that seek to capture market swings. For the long‐term approach, I’d lean on fundamental and macroeconomic data - think big energy and financial names - and update the portfolio periodically based on how those companies are performing relative to economic trends. The short‐term side would concentrate on a handful of liquid stocks or ADRs, looking for entry and exit points around news events or market shifts, but without diving too deeply into advanced models. Ultimately, I want to compare how a patient, fundamental‐driven strategy fares over years against a more opportunistic, news‐ and price‐driven approach over weeks or months, using historical Russian market data to see which style holds up better through periods of volatility and changing economic conditions. | 1 |
| I want to build a short‐term prediction model for the Japanese stock market (Nikkei 225), focusing on its top 20 large‐cap stocks over a 30-day horizon, using 14-day RSI and 10/20-day moving-average crossover signals. | 1 |
| not decided yet | 1 |
| I want to make a dashboard that records and automatically updates the dashboard with recommendations and projections of the market (forecasting), as well as the projected highest return on investments. | 1 |
| I want to do something related to comparing fashion stock prices during previous recessions, but I'm not set on anything specific yet. | 1 |
| I want to build an intraday, ideally near real time, prediction model mainly for SPY, SPX, QQQ, NDX based on a couple strategies I am currently using. | 1 |
| I want to track stocks from banks in Ecuador | 1 |
| Build a natural language processing model that analyzes earnings call transcripts to predict stock price movements. Focus on Russell 1000 companies, extracting sentiment, management confidence indicators, and forward-looking statements to predict 30-90 day returns. | 1 |
| I want to build a stock ranking system using ML that predicts the top 5 performing stocks in the Nifty 50 over the next 30 days based on price momentum, volume spikes, and sentiment extracted from financial news headlines. | 1 |
| I would like to develop a machine learning model that predicts short-term (1-5 day) price movements in the technology sector of the S&P 500 by combining: Technical indicators (RSI, MACD, Bollinger Bands) Earnings surprise data and sentiment analysis of earnings calls News sentiment from financial news sources Options market data (put/call ratios, implied volatility) The model would focus on the top 20 tech stocks by market cap and aim to generate trading signals for a momentum-based strategy. I would evaluate performance using walk-forward testing and compare against a buy-and-hold benchmark. | 1 |
| A trading bot that keeps track of recent time series and updates trading policy | 1 |
| Multi-Factor ETF Rotation Strategy with Sentiment Analysis to identify favorable ETF rotation opportunities while managing downside risk | 1 |
| Using macroeconomic data, news sentiment, and technical indicators like the RSI and MACD, I want to develop a machine learning model that can forecast short-term price changes in the South African stock market (JSE.JO). The goal is to evaluate the model's performance to that of a straightforward moving average strategy and produce actionable buy/sell recommendations for a 30-day timeframe. | 1 |
| I want to be able to crack currency trading using machine, its a fast and unpredictable | 1 |
| CAC 40 Alpha Generator: Building an End-to-End Quantitative Trading System | 1 |
| I want to experiment a short-term prediction model for the US/EU stock markets, focusing on the largest stocks over a 30-day investment horizon. I plan to use RSI and MACD technical indicators. Not sure what news sources to use to generate predictions. | 1 |
6. Investigate new metrics
138 / 138 correct (100.0%)
| Answer | Count |
|---|---|
| - | 2 |
| one metric I am thinking of right now is debt to liability, because private market is booming recently. another thing will be related to beta, or implied volatility. the volatility measurement would be the y (or part of y) in ML model instead of X, because sharp ratio might be more relevant than pure return when analyzing about 10K filings. | 1 |
| I plan to explore sentiment scores from financial news, trading volume, and volatility indices like VIX, as these metrics may enhance stock trend predictions and reflect investor behavior more accurately. | 1 |
| New metrics: RSI, MACD | 1 |
| the Volume metric change YOY/MOM, indicate investor/speculator saturation of a market | 1 |
| N/A | 1 |
| News Sentiment Score Why Useful: Positive or negative sentiment in headlines and articles often influences short-term stock price direction. How to Retrieve: Use NewsAPI, Finviz, or web scrape financial news sites using BeautifulSoup. NLP sentiment analysis can be applied using TextBlob or VADER in Python. | 1 |
| Given my focus on portfolio construction and diversification, I plan to explore additional metrics that help assess overlap and correlation between assets. Useful time series could include: Rolling correlation between assets: to track how asset relationships evolve over time and avoid concentration risk. Average True Range (ATR) or Volatility indices: to better understand asset risk profiles and adjust weights accordingly. Sharpe Ratio over rolling windows: to evaluate risk-adjusted performance dynamically. These metrics can be calculated using data from Yahoo Finance or other APIs like Alpha Vantage. I would use yfinance to fetch historical prices, then compute the metrics using pandas and numpy. For example, rolling correlations could be generated via df.rolling(window=30).corr() on log returns. These metrics support my project’s goal of algorithmic portfolio management without relying on explicit ML predictions. | 1 |
| Relative Strength Index (RSI) is a momentum indicator that identifies overbought or oversold conditions, which can signal reversals. | 1 |
| I think a couple metrics that could be valuable for my project are the Bank of Canada Interest Rate and CAD/USD Exchange Rate. I think I'll also look into sentiment analysis if it fits. The Bank of Canada Interest Rate and CAD/USD exchange rate most likely has a large impact on the Canadian stock market and I think these metrics will help to create a more robust model. I would use the FRED API for the Bank of Canada Interest Rate data and yfinance for the exchange rate. Sentiment analysis would likely be very powerful but it is most likely very difficult to pull off, to start out I would probably scrape news sites using the requests library and BeautifulSoup. | 1 |
| in addition to RSI / MACD, we would look at momentum / z-score, assess market beta | 1 |
| Historical Volatility Analysis, Volume Profile Analysis, Relative Strength Between Sectors, Momentum and Technical Indicators | 1 |
| I am reading about some models such as DSGE (Dynamic Stochastic General Equilibrium | 1 |
| 1) RSI measures how strongly a stock is being bought or sold in recent days and 2) MACD (Moving Average Convergence Divergence) shows trend direction and strength by comparing two exponential moving averages, both can be calculated with "ta" package. | 1 |
| Trading Volume, Volatility, News Sentiment Scores | 1 |
| For the news data, I will derive sentiment scores perhaps from social media like Reddit or other financial news source (about the stock itself, the industry and the general market). Also, maybe use the time to results announcement (days before/after), financial statement data, etc. to become features for ML model. | 1 |
| I need to identify the list of ETFs in the US market which are not correlated much. Then I can compare their prices relative to SP500 ETFs to identify the relative market winners at the time of the analysis. By analysing the market winner I can then adjust my portfolio. I am not sure what metric to use to identify the relative winners. So any suggestion from your side would be valuable. | 1 |
| levarage ratio | 1 |
| implied volatility, trading volume, vix index | 1 |
| I have an idea to download 60+ macroeconomic indicators from the article "Macroeconomic Indicators Affecting Stock Market" and analyze the correlation with gold, oil, commodities, currencies and indices https://pythoninvest.com/long-read/macro-indicators-affecting-stock-market | 1 |
| My idea is to explore a bit more about the sector of energy and tech, so for that i saw that using the exchange rate USD/COP, the colombia price index (CPI), the GDP which could help us know how much the country is growing The unemployment rate, Inflation rate In the repo i leave some examples on how to retrieve this data using python | 1 |
| Introduzirei a “Forward P/E Z-Score”. Essa métrica captura o desvio de valuation relativo ao histórico recente; valores > +1 σ indicam sobreavaliação (sinal de trim ou rotação), enquanto valores < -1 σ sugerem desconto (oportunidade de entrada). Será usada como gatilho adicional para ajustar o posicionamento previsto pelo classificador setorial. | 1 |
| CBOE SKEW index (^SKEW). Reason: High values imply left-tail panic. Helps flag “volatile-down” regimes early. | 1 |
| I think having all the tarrif rates around the world would be helpful for my analysis. However, scrapping the web for news will be helpful for enhancing the accuracy of the predictions | 1 |
| As part of my project, I explored a few additional metrics that could help improve correction prediction: Volatility Index (VIX): This index reflects market sentiment and fear. Sudden spikes in VIX often precede corrections. I can download historical VIX data using yfinance (^VIX) and join it with S&P 500 data to assess lead-lag relationships. Market Breadth Indicators: I plan to look at the number of advancing vs. declining stocks in the S&P 500 using data from Finviz or ETF proxies. Narrowing participation often signals weakening momentum. Sentiment Scores from News Headlines: Using NLP sentiment analysis on headlines from sources like Yahoo Finance or FinBERT could offer signals of investor mood shifts. Credit Spreads (Baa vs AAA Bonds): Rising credit spreads often signal stress in the economy. I would retrieve this data from FRED using pandas_datareader. Insider Trading Activity: Surges in insider selling can foreshadow downturns. While not directly accessible via public APIs, I can explore SEC EDGAR data scraping for this. These metrics are chosen to balance technical, macro, and sentiment dimensions. I would use Python libraries like yfinance, pandas_datareader, and BeautifulSoup to automate data retrieval. | 1 |
| For this exploratory question, I looked into two additional technical indicators that could provide valuable insights for stock analysis: the MACD (Moving Average Convergence Divergence) and simple moving averages (SMA). The MACD is a trend-following momentum indicator that helps identify potential entry and exit points by highlighting shifts in the strength or direction of price movements. It is especially useful for spotting potential reversals. Simple moving averages, such as the 50-day and 200-day SMAs, smooth out price fluctuations and help detect medium- and long-term trends. Crossovers between short- and long-term averages can also serve as trading signals. Both indicators can be derived from historical price data and complement traditional price and volume analysis by providing a broader view of market behavior. | 1 |
| To enhance the "buy-the-dip" strategy simulation, I would include the following additional metrics: S\&P 500 historical prices: Core data needed to detect market drops and simulate buy points. Rolling maximum and drawdowns: Helps identify how far the market has fallen from its recent peak. Volatility (e.g., 30-day std deviation): Useful for gauging market risk and filtering high-risk periods. Risk-free rate (e.g., T-bill rate): Provides a benchmark to compare the performance of the dip strategy. S\&P 500 dividend yield: Important for calculating total returns, not just price appreciation. These metrics help in building a more realistic and informative strategy evaluation. | 1 |
| To enhance my capstone project on short-term earnings-driven stock return prediction for major US tech companies, I plan to explore the following additional metrics and time series: 1. Implied Volatility (IV) from Options Data: IV reflects market expectations of future volatility around earnings announcements. Including IV could improve the model’s ability to anticipate price swings. Data source & retrieval: Use the yfinance Python package or APIs like Polygon.io or IEX Cloud to download options chains data. For example, with yfinance: ticker = yf.Ticker("AMZN") options_dates = ticker.options iv_data = ticker.option_chain(options_dates[0]).impliedVolatility # Extract implied volatility for near-term expiry 2. Trading Volume and Volume Spike Indicators: Unusual volume spikes often accompany earnings surprises and can signal strong market reactions. Volume metrics will complement price and sentiment data. Data source & retrieval: Volume data is part of historical price data available from yfinance: price_data = yf.download("AMZN", start="2023-01-01", end="2024-01-01")[['Close', 'Volume']] 3. Market Sentiment Scores from News and Social Media: Extracting sentiment from financial news articles, analyst reports, or Twitter posts around earnings dates could capture investor mood and expectations not reflected in price alone. Data source & retrieval: Use APIs such as NewsAPI, Twitter API (v2), or web scraping tools to collect text data, then apply NLP sentiment models like VADER or fine-tuned transformers (e.g., FinBERT). Example with NewsAPI: from newsapi import NewsApiClient newsapi = NewsApiClient(api_key='YOUR_API_KEY') articles = newsapi.get_everything(q='Amazon earnings', from_param='2025-04-28', to='2025-04-30') 4. Macroeconomic Indicators (e.g., interest rates, inflation, consumer sentiment): Broader economic conditions influence investor behavior and stock price reactions. Including these factors could help differentiate earnings reactions during different market regimes. Data source & retrieval: Use fredapi for Federal Reserve Economic Data (FRED): from fredapi import Fred fred = Fred(api_key='YOUR_FRED_API_KEY') cpi = fred.get_series('CPIAUCSL', start_date='2023-01-01') These metrics are valuable as they provide multidimensional signals—market expectations (IV), actual trading activity (volume), investor sentiment (news and social media), and macro environment context—which collectively could enhance prediction accuracy for earnings-related stock moves. | 1 |
| 1. YouTube Transcripts Why useful: Raw material for detecting management tone, guidance changes, and competitive mentions in real-time. Python retrieval: python from youtube_transcript_api import YouTubeTranscriptApi transcript = YouTubeTranscriptApi.get_transcript(video_id) 2. Analyst Consensus Estimates Why useful: Provides benchmark to quantify earnings surprises (actual vs expected). Python retrieval: python import yfinance as yf estimates = yf.Ticker("AAPL").analyst_price_target 3. SEC Filing Financials Why useful: Ground truth data to validate management claims from earnings calls. Python retrieval: python from sec_edgar_downloader import Downloader dl = Downloader().get("10-Q", "AAPL") 4. Short Interest Ratio Why useful: Signals market sentiment and potential volatility around earnings events. Python retrieval: python import nasdaqdatalink short_data = nasdaqdatalink.get(f"FINRA/FNYX_AAPL") 5. Institutional Ownership Why useful: Helps assess whether large investors are aligning with management commentary. Python retrieval: python ownership = nasdaqdatalink.get("ML/OWN_AAPL") Key Pattern: Each metric either: Provides real-time content (YouTube), Offers validation data (SEC filings), or Adds market context (analyst/sentiment data) - creating a 360° view of earnings events. Retrieval uses specialized Python libraries for each data source. | 1 |
| This project proposes the investigation of the Investor Sentiment Index as a leading predictive metric for equity market behavior. The study will assess the correlation between sentiment shifts and short-term market movements, aiming to integrate sentiment-based indicators into quantitative trading strategies or market risk models. | 1 |
| Conditional Value-at-Risk (CVaR) / Expected Shortfall (ES) | 1 |
| VIX Index (Volatility Index) Measures market risk. High VIX values may correspond with erratic price movements, especially in small caps. | 1 |
| 1. VIX and VIX Term Structure Source: CBOE, Yahoo Finance (^VIX, ^VIX9D, ^VIX3M) Rationale: Volatility regime detection is crucial for pairs trading. High VIX periods often coincide with mean-reversion opportunities, while low VIX periods may favor momentum strategies. The VIX term structure (contango vs. backwardation) provides insights into market stress and expected volatility persistence. 2. Sector Momentum Indicators Source: SPDR Sector ETFs (XLK, XLF, XLE, XLV, XLI, XLP, XLY, XLU, XLB, XLRE, XLI) Metrics: Relative strength ratios between sectors Sector rotation velocity (rate of leadership changes) Cross-sector correlation matrices Rationale: Chan emphasizes that pairs trading works best when stocks are from the same industry. Understanding sector rotation helps identify when traditional correlations break down and when pairs might diverge due to fundamental sector shifts rather than mean-revertible noise. 3. Interest Rate Environment Metrics Source: FRED (Federal Reserve Economic Data) Metrics: 10-Year Treasury yield (^TNX) 2-10 Year yield curve spread Real rates (TIPS-Treasury spread) Fed Funds Rate expectations (from CME FedWatch) Rationale: Interest rate regimes significantly impact sector rotations (growth vs. value, financials vs. utilities). Different rate environments create different correlation structures between stocks and sectors, affecting pairs trading profitability. 4. Options Flow and Put/Call Ratios Source: CBOE, Yahoo Finance Metrics: CBOE Put/Call Ratio (^PCR) Individual stock put/call ratios Options volume relative to stock volume Implied volatility skew Rationale: Options activity provides forward-looking sentiment indicators. Extreme put/call ratios often coincide with market turning points, which are optimal entry points for mean-reversion strategies. This data can help time entries and exits more effectively. 5. Market Breadth Indicators Source: NYSE, NASDAQ data Metrics: Advance/Decline ratio New Highs/New Lows ratio Percentage of stocks above 50/200-day moving averages Up volume vs. Down volume Rationale: Market breadth helps distinguish between broad market moves (which affect all pairs similarly) and idiosyncratic moves (which create pairs trading opportunities). Poor breadth during market rallies often signals regime changes that could affect pairs correlations. Data Collection Strategy: I plan to use a combination of: yfinance library for basic market data and ETF prices FRED API for economic indicators Alpha Vantage or Quandl for additional technical indicators Yahoo Finance for options data and breadth indicators This comprehensive dataset will enable the machine learning model to detect market regimes and adapt pairs trading parameters accordingly, following Chan's philosophy of using quantitative methods to systematically exploit market inefficiencies. | 1 |
| For this project, I plan to investigate the following additional metrics: Retail Investor Attention Metrics Data Source: Reddit API (e.g., r/wallstreetbets post counts), Twitter API (stock ticker mentions) Why useful: These metrics can proxy investor attention, which is known to precede large inflows and price changes. Python Tool: praw for Reddit, tweepy or X API for Twitter Example Code: python Copy Edit import praw reddit = praw.Reddit(client_id='xxx', client_secret='xxx', user_agent='herd-detector') posts = reddit.subreddit('wallstreetbets').search('AMZN', time_filter='week') post_count = sum(1 for _ in posts) Short Interest Ratio Data Source: Yahoo Finance or MarketWatch Why useful: High short interest may indicate pressure that can trigger short squeezes when retail herding occurs. Python Tool: yfinance Example Code: python Copy Edit import yfinance as yf ticker = yf.Ticker("AMZN") print(ticker.info.get("shortRatio")) | 1 |
| RSI is good metrics. I will calculate it using already obtained data. | 1 |
| For my agent-based trading project focused on Indonesian bank stocks and the IHSG, I explored a variety of additional metrics and time series beyond standard price data to strengthen my trading agent’s decision-making capabilities. Technical indicators such as RSI, moving averages, and Bollinger Bands are particularly valuable for identifying momentum, trend strength, and potential reversal points—these can be easily calculated from OHLCV data using Python libraries like pandas and ta. In addition to technicals, I looked into volatility measures such as the VIX (for global context) and local realized volatility, as periods of high volatility often correspond to larger price swings and may require the agent to adjust risk exposure or trading frequency. I also considered macroeconomic metrics like the BI 7-Day Reverse Repo Rate, inflation rate, and USD/IDR exchange rate, since these have a direct impact on the Indonesian banking sector’s fundamentals and can influence market sentiment. To capture shifts in market mood, I explored using sentiment analysis on Indonesian financial news and social media—retrieved via APIs like NewsAPI or web scraping—and generated sentiment time series to serve as additional features for the trading agent. Furthermore, I assessed foreign net buy/sell activity in banking stocks, available from IDX or broker data, since significant foreign flows can drive short-term price moves. For data retrieval, I used yfinance for historical price and volume data, as well as for calculating most technical indicators. Macroeconomic data was sourced from Bank Indonesia and processed with pandas. Sentiment data was obtained using news APIs combined with basic NLP in Python, and correlation analysis between stocks and macro factors was performed with pandas.corr(). By systematically integrating these diverse data streams, my agent can generate more informed trading signals, adapt dynamically to market conditions, and manage risk more effectively in the Indonesian market. | 1 |
| For my forecasting project, I plan to explore and integrate several additional time series and metrics that could improve weekly stock price predictions: Earnings Calendar (Microeconomic Events) I will use earnings date data (e.g., from Yahoo Finance or Nasdaq) to identify periods with potential volatility. These can be used as binary features (e.g., "earnings this week = 1"). Macroeconomic Event Calendar I intend to include scheduled macro events like CPI, GDP, FOMC meetings (via tradingeconomics.com or Investing.com). These can be encoded by week and could help model volatility shifts. Volatility Index (VIX) Weekly VIX levels (e.g., via yfinance symbol ^VIX) will be used to gauge general market sentiment and fear. This could be useful in determining expected ranges and risk levels. Volume and Volatility Stats For each stock, I will calculate 7-day rolling volatility and volume changes to capture recent activity and momentum. Seasonal Patterns I may include time features like week number, month, and "week of earnings season" as dummy variables to detect seasonal effects. How to retrieve data: I will use Python packages such as yfinance, pandas_datareader, and web scraping via requests/BeautifulSoup when needed. Calendars can be transformed into feature matrices aligned with each trading week. | 1 |
| Considering my answer to the Q5, the crypto trading data can be retrieved using public API such as CoinGecko | 1 |
| Financial statements, social media sentiment, macro/sector factors | 1 |
| Volume Spike Ratio Compares post-earnings volume to average volume — high ratios often confirm institutional interest. Price Efficiency Score Assesses how quickly price incorporates earnings news — inefficient reactions could signal delayed trade opportunities. | 1 |
| https://github.com/jelambrar96-datatalks/stock-markets-analytics-zoomcamp/blob/hw-01/cohorts/2025/homework_1.ipynb | 1 |
| I would use Moving Average Crossover, Relative Strength Index and Moving Average Convergence Divergence because these indicators are widely used in technical analysis and provide interpretable, time-tested signals that can enhance the predictive power of machine learning models in intraday trading | 1 |
| For my proposed capstone project, which focuses on developing a machine learning-based trading strategy for mid-cap technology stocks in the U.S. market over a 90-day investment horizon, I’ll investigate three additional metrics or time series that could enhance the model’s predictive power. These metrics will complement the technical indicators (RSI, MACD, Bollinger Bands), fundamental data (revenue growth, EPS), sentiment data (news and X posts), and macro data (interest rates, VIX) outlined in the project. I’ll describe each metric, explain its usefulness, and provide Python code to retrieve it, demonstrating my ability to generate data requests and locate relevant data sources. The current date and time are May 31, 2025, 8:35 PM SAST. | 1 |
| Since I am focusing on the European stock market, I plan to go beyond company-level data and include broader market metrics that may help explain short-term stock movements after earnings announcements. In particular, I will look at major indices like the DAX, CAC 40, and STOXX Europe 600 to capture overall market conditions. I will also include volatility indicators such as the VSTOXX and VDAX-NEW, which reflect investor uncertainty in the European context. To explore behavioral factors, I intend to use Google Trends to track public interest in specific stocks before earnings releases. In addition, I find it relevant to monitor carbon pricing in the EU, especially for companies in emissions-sensitive sectors like energy or airlines. Although my focus is on European stocks, I believe it’s essential to consider the influence of the U.S. market, particularly given the time zone differences—U.S. markets may open after European ones close, and their reactions can spill over into the next day’s trading in Europe. | 1 |
| I selected Yahoo Finance and FRED as data sources to retrieve time series data for the NASDAQ Composite Index (^IXIC), the VIX volatility index, and the 10-year U.S. Treasury yield. The NASDAQ reflects growth-oriented market performance, VIX captures market sentiment and volatility, and the Treasury yield influences asset pricing and discount rates. These datasets can be accessed via Python using libraries such as yfinance and fredapi for further modeling and analysis. | 1 |
| Volatility Metrics; Sentiment Analysis; On-chain Metrics for Crypto; Liquidity Metrics; Macro-Economic Indicators; | 1 |
| GBP/USD Exchange Rate | 1 |
| Apart from standard macro indicators (inflation, interest rates, consumer sentiment, etc.) and popular technical indicators (RSI, Stochastic RSI, MACD, Bollinger Bands, MAs, etc.) I would like to experiment and explore the predictive power of oil price (WTI), DXY index and COT (https://publicreporting.cftc.gov/stories/s/r4w3-av2u) reports, which show the market sentiment among big and small players as well as hedging funds (available csv files). Also, the crude oil inventories and/or production in the USA might be a good predictor (https://www.eia.gov/opendata/browser/petroleum/stoc/wstk). | 1 |
| All time high | 1 |
| Unfortunately, I've to skip this question this time. | 1 |
| Amazon daily close price | 1 |
| not yet | 1 |
| I would definitely like P/E ratios and analyst ratings, which I believe are available in yfinance, although i don't know if they are historical/dated. | 1 |
| I plan to use additional time series metrics like Average True Range (ATR) to capture volatility, On-Balance Volume (OBV) to gauge buying/selling pressure, and Rate of Change (ROC) to track momentum shifts. These can be computed directly from price and volume data using Python libraries like ta. They may improve short-term prediction accuracy by highlighting trend strength and reversals. | 1 |
| I am trying to use twitter or reddit sentimental signal and find out the correlation in the past | 1 |
| I'll be using majorly ohlcv data and momentum indicators with the ccxt crypto exchanges APIs interface, which handles ratelimit, order placement and every endpoint the exchange API themselves provide. | 1 |
| To analyze crypto trends, I look at technical indicators like the RSI to check if a coin is overbought or oversold, the MACD to spot momentum shifts, and Bollinger Bands to see price volatility and potential breakouts. | 1 |
| smape | 1 |
| these 2: ICSA Initial Claims IC4WSA 4-Week Moving Average of Initial Claims because unemployment is rising in usa | 1 |
| There are several metrics that can be used in stock market analysis. If we are trading in a long term horizon we should always consider we must include to our technical analysis, fundamental analysis. In this case we should include metrics such as book value, total debt, total cash flow, Beta. This last metric is very interesting to include in the analysis because it should be a decision factor in long term trading. We can find these metrics in several web pages and scrap them using python. | 1 |
| There are a lot of metrics including costs of raw materials, risk by country or region. Tracking the ppi and cpi by sector could help narrow this down. | 1 |
| This could be the predicted x-value, where x stands for several date spans | 1 |
| I would like to focus on European stock market indexes (Stoxx 600, DAX, FTSE 100) and identfiy up to 20 of the largest companies in this sector. Then use web scraping tools to access company earnings, news announcements and company reports. Some metrics to investigate: Gross Margin per Project Type Return on Invested Capital (ROIC) Average Project Duration Sustainability Metrics | 1 |
| VIX index, I would use yfinance to get this index which compute the market's volatility | 1 |
| hit ratio, risk-reward ratio | 1 |
| I haven't conducted any analysis on different datasets yet; however, I downloaded earnings dates from Yahoo Finance to supplement the missing Amazon data and to correct the foundational dates in the S&P 500 exercise available. I have investigated new metrics as kernels and find them interesting to apply them to my capstone project | 1 |
| Using financial statements, I calculated various ratios like Current Ratio, Quick Ratio (Acid-Test Ratio), Cash Ratio, Debt-to-Equity Ratio, Debt-to-Asset Ratio | 1 |
| Currently I am still thinking of possible new metrics, will present in week 2 homework | 1 |
| Volatility Index (VIX) Why it's useful? The VIX measures expected market volatility over the next 30 days and often spikes during market stress. It’s a leading indicator of investor sentiment and risk. import yfinance as yf ticker = yf.Ticker("AMZN") vix = ticker.history(period="max", interval = "1d") plt.figure(figsize=(15, 4)) sns.lineplot(x=vix.index, y=vix.Close) plt.title("VIX - Market Volatility Index"); | 1 |
| Captures the difference between actual macroeconomic data and market expectations. Positive surprises often lead to short-term asset rallies | 1 |
| Correlation between market index, inflation, world and individual portfolio or stock to see if it is in line with the world markets and impacted by events and inflation. | 1 |
| The VIX, often referred to as the “fear index,” measures market expectations of near-term volatility conveyed by S&P 500 option prices. It provides insight into investor sentiment and potential risk-off behavior. Bond yields influence the discount rate used in stock valuation models. A rising 10Y yield can signal tightening financial conditions, affecting equities, especially growth stocks. | 1 |
| Inflation adjusted return = Nominal Return – Inflation Rate (To show the actual purchasing power of your returns), ROI(Return on Investment) = ((Final Value – Initial Investment) / Initial Investment) × 100 | 1 |
| i would collect Asset Price Data (Equities, Bonds, ETFs), Volatility Index, Interest Rates for some period with using yfinance and pandas_datareader; form portfolio and will try to predict future value with ARIMA model | 1 |
| The unemployment rate is very relevant to the overall performance of the stock market. The data can be extracted from https://fred.stlouisfed.org/ | 1 |
| I’d start by pulling in daily Brent crude futures and USD/RUB spot rates - two core drivers of Russian corporate profits and market sentiment (please see all the code in the homework notebook). Since inflation affects real returns, I’d grab Russia’s monthly CPI from World Bank. To see how bond yields influence equity multiples, I’d download the MOEX Russia Government Bond Index. Finally, a simple RSS scraper can capture headline sentiment from a Russian business site (e.g., RBC). | 1 |
| Nikkei Volatility Index, USD/JPY Exchange Rate, Japan 10‐Year Government Bond Yield, etc. | 1 |
| Free Cash Flow: cash left over after a company pays for OpEx and CapEx. Would be helpful to see how much money the company has in reserves. | 1 |
| I think highs and lows at time periods (during recessions vs not) would be my main metric. I think I would retrieve it with the yfinance tool at least for the last 25 years. | 1 |
| Social media sentiment from Reddit/Twitter/StockTwits - can predict short-term volatility and momentum | 1 |
| Explore the relationship between short interest ratio (SIR) and future stock performance. High SIR might indicate upcoming volatility or contrarian opportunities. | 1 |
| Fear & Greed Index (from CNN Money) - Useful for market sentiment analysis | 1 |
| Gradient of movement of plateaus (high - low - high) over time | 1 |
| Sector Fund Flows Data, Bitcoin hash rate or others that provide fast investors sentiment | 1 |
| Some additional metrics I would consider: - Volatility Index (VIX): Useful for gauging market sentiment and risk. - Put/Call Ratio: Indicates investor sentiment. Data can be scraped from financial websites or APIs. - Foreign Institutional Investor (FII) Flows: Important for emerging markets like India. Data available from NSE or financial news APIs. - Interest Rates and Inflation: Macro factors affecting stock prices. | 1 |
| at least Volatility Index | 1 |
Calculated: 9 June 2025, 15:34