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First stable version v1.0.0
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‎.gitignore‎

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# Python
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__pycache__/
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*.py[cod]
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*.pyo
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*.pyd
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*.so
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# Virtual environments
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venv/
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.env/
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.venv/
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venv-fintech/
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# Jupyter / Streamlit cache
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.ipynb_checkpoints
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.streamlit/
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.cache/
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**/__pycache__/

‎CHANGELOG.md‎

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# Changelog
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All notable changes to this project will be documented in this file.
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This project adheres to [Semantic Versioning](https://semver.org/).
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---
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## [Unreleased]
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### Added
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- (placeholder for upcoming features)
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---
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## [v1.0.0] - 2025-11-02
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### Added
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- Initial Streamlit dashboard structure
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- Modular architecture (`modules/data`, `modules/analysis`, `modules/visualization`, `modules/ui`)
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- Real-time data via Yahoo Finance (`yfinance`)
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- Linear regression forecasting
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- RSI and SMA (20/50) indicators
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- Asset selector covering indices, commodities, crypto, bonds, and ETFs
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- Plotly visualizations (candlestick, RSI, forecast)
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- User Guide tab
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- README, LICENSE (MIT), and .gitignore
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### Fixed
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- None (initial release)
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### Changed
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- None (initial release)
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### Removed
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- None (initial release)

‎LICENSE‎

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MIT License
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Copyright (c) 2025 <Tu Nombre>
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE.

‎README.md‎

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# 💹 FinMarket Analyzer
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> ⚠️ **Disclaimer:** This project is still under active development and continuous evolution.
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---
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An interactive **Python & Streamlit dashboard** for exploring and analyzing global financial markets.
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It provides real-time insights, technical indicators, and linear trend forecasting across equities, commodities, crypto, fixed income, and thematic ETFs.
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---
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## 🚀 Features
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- 📈 **Interactive dashboards** built with Streamlit and Plotly
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- 💰 **Multi-asset support** — equities, commodities, crypto, fixed income, thematic ETFs
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- 📊 **Technical indicators** — Simple Moving Averages (SMA), RSI
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- 🔮 **Forecasting** — basic linear regression trend prediction
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- 🧱 **Modular architecture** — easy to extend with new models or analytics
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- 💾 **Cached data** for performance via Streamlit’s cache system
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---
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## 🧠 Tech Stack
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- **Python 3.12+**
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- [Streamlit](https://streamlit.io)
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- [Plotly](https://plotly.com/python/)
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- [yfinance](https://github.com/ranaroussi/yfinance)
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- [pandas](https://pandas.pydata.org/)
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- [scikit-learn](https://scikit-learn.org/)
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---
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## 🏗️ Installation
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Clone the repository and install the dependencies:
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```bash
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git clone https://github.com/<your-username>/finmarket-analyzer.git
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cd finmarket-analyzer
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pip install -r requirements.txt
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```
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## ▶️ Run the App
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```bash
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streamlit run finmarket_app/app.py
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```
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## Author
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**AlejandroDiazD**
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[GitHub](https://github.com/AlejandroDiazD)
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## License
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This project is licensed under the **MIT License** — see the [LICENSE](LICENSE) file for details.
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You are free to use, modify, and distribute this software, provided that the original license and copyright notice are included.

‎finmarket_app/app.py‎

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import streamlit as st
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import pandas as pd
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from modules.data.data_loader import load_data
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from modules.analysis.indicators import add_technical_indicators
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from modules.analysis.metrics import get_quick_metrics
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from modules.analysis.forecast import make_linear_forecast
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from modules.visualization.plots import plot_candlestick, plot_rsi, plot_forecast
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from modules.ui.user_guide import render_user_guide
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from modules.ui.utils_ui import format_asset_title
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st.set_page_config(page_title="Market Insights Dashboard", layout="wide")
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st.title("📊 Market Insights Dashboard")
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# --- Sidebar ---
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st.sidebar.header("Settings")
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# Dropdown for asset selection
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asset_options = {
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# ---- Global Indices ----
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"S&P 500": "^GSPC",
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"NASDAQ 100": "^NDX",
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"MSCI World": "URTH",
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"MSCI Emerging Markets": "EEM",
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# ---- Commodities ----
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"Gold": "GC=F",
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# ---- Cryptocurrencies ----
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"Bitcoin": "BTC-USD",
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"Ethereum": "ETH-USD",
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# ---- Fixed Income ----
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"EU Gov Bonds 1-3Y": "EUNA.AS", # working ETF replacement
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# ---- Thematic ETFs ----
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"VanEck Uranium & Nuclear ETF": "NLR",
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"iShares Global Clean Energy": "ICLN",
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}
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asset_name = st.sidebar.selectbox("Select asset", list(asset_options.keys()))
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ticker = asset_options[asset_name]
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period = st.sidebar.selectbox("Period", ["1y", "5y", "10y", "max"])
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interval = st.sidebar.selectbox("Interval", ["1d", "1wk", "1mo"])
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st.sidebar.header("Forecast Settings")
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forecast_horizon = st.sidebar.slider("Horizon (steps)", 7, 90, 14)
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forecast_window = st.sidebar.slider("Training window (points)", 30, 365, 120)
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# --- Tabs ---
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tab_dashboard, tab_forecast, tab_guide = st.tabs(["📈 Dashboard", "🔮 Forecast", "📘 User Guide"])
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with tab_dashboard:
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data = load_data(ticker, period, interval)
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if data.empty:
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st.error("⚠️ No market data available for this ticker.")
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st.stop()
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data = add_technical_indicators(data)
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display_title = format_asset_title(asset_name, ticker)
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st.plotly_chart(plot_candlestick(data, display_title), width='stretch')
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st.subheader("📉 Relative Strength Index (RSI)")
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st.plotly_chart(plot_rsi(data), width='stretch')
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st.subheader("📈 Quick Metrics")
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col1, col2, col3 = st.columns(3)
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price, change_week, rsi = get_quick_metrics(data)
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col1.metric("Current Price", f"{price:.2f}")
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col2.metric("1-Week Change", f"{change_week:.2f}%")
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col3.metric("Current RSI", f"{rsi:.2f}")
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with tab_forecast:
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st.subheader("🔮 Simple Linear Forecast")
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data = load_data(ticker, period, interval)
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if data.empty:
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st.warning("No data available to build a forecast.")
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st.stop()
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fc = make_linear_forecast(data, interval, forecast_window, forecast_horizon)
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if fc.empty:
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st.warning("Not enough data to produce a forecast.")
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else:
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st.plotly_chart(plot_forecast(data, fc, display_title), width='stretch')
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st.info("This forecast uses a simple linear regression trend based on recent data.")
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with tab_guide:
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render_user_guide()

app.py renamed to finmarket_app/config/settings.py

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utils/data_loader.py renamed to finmarket_app/modules/analysis/__init__.py

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‎finmarket_app/modules/analysis/forecast.py‎

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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from sklearn.linear_model import LinearRegression
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def _infer_horizon(interval: str, default: int = 14) -> int:
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return default
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def _ensure_series(close: pd.Series | pd.DataFrame) -> pd.Series:
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if isinstance(close, pd.DataFrame):
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close = close.squeeze()
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return close
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def make_linear_forecast(
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data: pd.DataFrame,
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interval: str = "1d",
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window: int | None = None,
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horizon: int | None = None
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) -> pd.DataFrame:
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"""
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Build a simple univariate linear-regression forecast on the Close price.
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- Uses the last `window` points to fit a linear trend.
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- Projects the next `horizon` steps.
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- Adds naive confidence bands from residual std (not statistically rigorous).
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Returns
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-------
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pd.DataFrame
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Forecast DataFrame with index = forecast dates and columns:
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['yhat', 'yhat_lower', 'yhat_upper']
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"""
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if data is None or data.empty:
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return pd.DataFrame()
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close = _ensure_series(data["Close"]).dropna()
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if close.empty or len(close) < 10:
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return pd.DataFrame()
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n = len(close)
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if window is None:
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window = max(30, int(n * 0.5)) # use half the series or at least 30
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window = min(window, n)
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if horizon is None:
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horizon = _infer_horizon(interval, default=14)
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# --- Prepare training data ---
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y = close.iloc[-window:].values # shape (window,)
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X = np.arange(window).reshape(-1, 1)
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# --- Fit linear model ---
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model = LinearRegression()
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model.fit(X, y)
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# --- Compute in-sample residuals ---
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y_hat_in = model.predict(X)
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resid = y - y_hat_in
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sigma = np.std(resid) if len(resid) > 1 else 0.0
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# --- Predict future ---
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X_fut = np.arange(window, window + horizon).reshape(-1, 1)
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y_hat = model.predict(X_fut)
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# --- Confidence bands (approx. ±1.96σ) ---
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y_lower = y_hat - 1.96 * sigma
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y_upper = y_hat + 1.96 * sigma
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# --- Build future index ---
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last_ts = close.index[-1]
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if close.index.inferred_type in ("datetime64", "datetime64tz"):
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freq_map = {"1d": "D", "1wk": "W", "1mo": "MS"}
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freq = freq_map.get(interval, "D")
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future_index = pd.date_range(last_ts, periods=horizon + 1, freq=freq)[1:]
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else:
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future_index = pd.RangeIndex(n, n + horizon)
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# --- Build output DataFrame ---
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out = pd.DataFrame(
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{"yhat": y_hat, "yhat_lower": y_lower, "yhat_upper": y_upper},
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index=future_index
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)
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# ✅ Clean and sort output (robustness)
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out = out.sort_index()
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out = out.apply(pd.to_numeric, errors="coerce")
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out = out.dropna(subset=["yhat"])
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return out

‎finmarket_app/modules/analysis/indicators.py‎

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import ta
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import pandas as pd
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def add_technical_indicators(data: pd.DataFrame) -> pd.DataFrame:
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"""
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Adds simple moving averages (SMA 20, SMA 50) and RSI.
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Handles MultiIndex columns from yfinance.
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"""
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if isinstance(data.columns, pd.MultiIndex):
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data.columns = [col[0] for col in data.columns]
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if isinstance(data["Close"], pd.DataFrame):
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data["Close"] = data["Close"].squeeze()
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data["SMA_20"] = ta.trend.sma_indicator(data["Close"], window=20)
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data["SMA_50"] = ta.trend.sma_indicator(data["Close"], window=50)
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data["RSI"] = ta.momentum.rsi(data["Close"], window=14)
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return data
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‎finmarket_app/modules/analysis/metrics.py‎

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import pandas as pd
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def get_quick_metrics(data: pd.DataFrame):
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"""
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Returns current price, weekly change, and RSI.
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"""
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current_price = data['Close'].iloc[-1]
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change_week = (data['Close'].iloc[-1] / data['Close'].iloc[-5] - 1) * 100
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current_rsi = data['RSI'].iloc[-1]
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return current_price, change_week, current_rsi
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