Apps & APIs
Crypto Market Analytics Dashboard & Forecasting Toolkit
Market analytics prototype that combines data extraction, technical indicators, forecasting experiments, portfolio analytics, and dashboard storytelling into one structured decision-support workspace.
Market research command view
Crypto Market Analytics Dashboard
Technical indicators, forecasting experiments, portfolio analytics, and dashboard storytelling in one market lab
Collect
Market data
Measure
Indicators
Forecast
Models
Analyze
Portfolio
Present
Dashboards
Signal board
Indicator mixRSI / MACD / OBV
ARIMA / LSTM
Risk / Allocation
Dash / Plotly
Research preview
Asset watchlist| Asset | Trend | Signal | Model | Status |
|---|---|---|---|---|
| BTC | Uptrend | RSI 64 | ARIMA | Active |
| ETH | Range | MACD + | SARIMA | Review |
| SOL | Momentum | OBV ↑ | LSTM | Watch |
| ADA | Reversal | Bands | RF | Test |
Story outputs
Business problem
Market research needed more than isolated charts or single-model experiments. The work needed a sandbox where historical market data, technical indicators, forecasting prototypes, and portfolio ideas could be reviewed together.
Without a structured analytics layer, it was harder to compare models, evaluate signals, or turn raw market movement into a more usable decision-support narrative.
System built
Built a Dash / Plotly analytics toolkit with market-data extraction, technical indicators such as RSI, MACD, Bollinger Bands, and OBV, plus forecasting prototypes including ARIMA, SARIMA, LSTM, and related experiments.
The result is a cleaner research workflow that connects signal generation, model experimentation, and portfolio-oriented analysis inside a dashboard experience that tells a stronger technical story.
Signal review
Signals reviewed
The dashboard evaluates market history, technical indicator behavior, model outputs, and portfolio context so analysis can move from raw price action to structured decision support.
Market analytics workflow
How it works
Collect
Pull market history and supporting price data into one reusable analytics workspace.
The workflow begins by collecting raw market data so technical indicators, forecasting experiments, and dashboard components all run on a shared foundation.
Measure
Calculate core technical indicators and market diagnostics that help describe momentum, trend, and behavior.
Indicator generation turns raw price movement into more interpretable signals that can be reviewed visually and compared across assets.
Forecast
Test forecasting approaches such as ARIMA, SARIMA, LSTM, and related prototype models.
The forecasting layer is built as an experimentation space, making it easier to compare methods and evaluate how different models behave under the same inputs.
Analyze
Review allocation, portfolio, and risk-oriented outputs that connect analytics work to decision support.
This layer turns model and indicator output into something more practical by framing the data around positioning, risk, and opportunity.
Present
Publish the results through a Dash / Plotly experience with charts, comparison views, and market-readiness summaries.
The final layer gives the project a decision-ready surface where signals, experiments, and portfolio insights can be reviewed together.
System layers
What the toolkit coordinates
Market data layer
Pulls price history and related market inputs into a reusable foundation for indicators, charts, and modeling.
Indicator engine
Calculates signals such as RSI, MACD, Bollinger Bands, and OBV so market movement becomes more interpretable.
Forecast lab
Provides a controlled sandbox for comparing ARIMA, SARIMA, LSTM, and related forecasting prototypes.
Dashboard layer
Packages the analysis into charts, portfolio views, and decision-support summaries through Dash and Plotly.
Impact signals
What the project improved
Technical indicator dashboard for market review
Forecasting prototype comparisons across models
Portfolio and allocation experimentation
Reusable visualization layer for analytics storytelling
Sandbox for connecting data science work to decision support
Operational value
From prototype analytics to a stronger market story
Signal clarity
Transforms raw market data into indicator-based views that are easier to interpret than looking at prices alone.
Model experimentation
Creates a controlled environment for testing and comparing forecasting approaches before committing to any single method.
Portfolio framing
Connects data science output to practical allocation and market review use cases instead of leaving it as isolated notebooks.
Visual storytelling
Presents the market workflow as a professional analytics story with charts, signals, model previews, and decision-support views.
Why this project matters
Market dashboards become more valuable when indicators, models, and portfolio thinking are connected in one coherent workflow.
This project is more than a crypto dashboard. It shows how research, analytics, and experimentation can be organized into a professional data product. By combining data extraction, technical indicators, forecasting prototypes, and portfolio views, the toolkit gives the analysis a clearer beginning, middle, and end.
The value is the story: collect the data, transform it into signals, test models, evaluate risk and opportunity, and present the output in a way that supports better review and sharper technical communication.
Confidentiality note
Visuals and descriptions are sanitized conceptual representations. They do not expose private company data, personal financial positions, live account credentials, proprietary model parameters, raw exports, internal notebooks, or source project secrets.