The Book of a Paper Trader: 355 trades, one honest question. Is there an edge?
ALPACA · PAPER ACCOUNT · 27 AUG – 30 OCT 2026 Most trading dashboards show you a P&L number and stop. This report keeps asking why. Over ten weeks an automated bot won +$23,859, and still closed the book at ▼ −$4,711. The story is about where that money went, which part of the system actually works, and what is sitting on the desk right now. Profit factor 0.84 · win rate 50.1% · expectancy ▼ −$13.27 per trade · account equity $95,819 The story in five pages Cover · What am I looking at? Three headline numbers, one candle per trading day, and a “how to read” strip for R-multiple, profit factor and why PAPER matters. The Book · Is the account growing? No. Stop exits alone erase 54% of all gains. September was the bleed (▼ −$6,828), October stopped it (▼ −$13). Only crypto pays (▲ +$631, PF 1.14). The Edge · Where do wins and losses come from? Shorts carry the book: ▲ +$4,727 at 62% win vs longs ▼ −$9,438 at 44%. 40 trades slipped past −1R (▼ −$8.2K), while trailing stops made ▲ +$2,845 at 81% win. The Desk · What is open right now? 74% of equity sits in cash ($71.3K). One name (KO) is 39% of exposure. 631 orders: 43.9% filled, 52% cancelled by design. The Ledger · Let me explore it myself. Every strategy by asset class with trades, a net P&L heat bar, win rate, avg R, profit factor and an EDGE / WATCH / CUT verdict. Storytelling choices Titles state the finding, not the chart type. “From $23.9K won to $4.7K lost”, “Shorts carry the book”, “One name carries the gain”. Every title is a DAX measure, so it rewrites itself when the data changes. One question per page, in the order a trader would ask it: am I growing → why → what is at risk now → let me dig. Honest framing. A PAPER badge on every page, the 15 crypto pairs with no price data are called out instead of hidden, and the cover explains every metric before you see it. Markers inside the chart instead of legends: PEAK / LOW chips on the equity curve, a win-rate chip on every strategy, “+N smaller” for treemap tiles too small to label. How it is built Microsoft Fabric end to end. Notebooks pull Alpaca (paper) and Binance data into a lakehouse and move it bronze → silver → gold as Delta tables. Two semantic models. A Direct Lake model for daily work, plus an Import copy for Publish to web (Direct Lake can't be published publicly). 146 DAX measures, zero custom visuals. Every chart (equity curve with drawdown, waterfall, lollipop, R-multiple dot plot, treemap, order-flow sankey, cover candles) is drawn in DAX as SVG or HTML. It is fully data-driven and loads as plain images. One design system. A single token set (colour, type scale, radius, 8px spacing) generates the DAX, a tokens.css file and the Power BI theme JSON, so native and custom visuals look identical. Serif display titles, sans labels, tabular figures. Navigation and accessibility. A dark rail with an active pill, made clickable by an invisible vertical Page navigator. Gain/loss colours pass WCAG AA, ▲▼ arrows mean colour is never the only signal, and alt-text measures are written for the main visuals. Explore ▶ Live report: embedded above (start on the cover and use the left rail) ↗ GitHub (notebooks, all DAX measures, theme and design tokens): github.com/PatricRc/trading-analytics-fabric ↗ LinkedIn: linkedin.com/in/patriciorioscanepa · Portfolio: data-pat-ai.netlify.app Paper trading only. Simulated fills, no real money, not financial advice. Built by Patricio Rios with Microsoft Fabric + Power BI.
Global Financial Analytics Dashboard
Global Financial Analytics Dashboard 🚀 Interactive Power BI Financial Dashboard built using a large-scale financial dataset containing over 50,000+ records. This dashboard provides comprehensive insights into financial performance, revenue trends, profit analysis, expense tracking, and key business KPIs through advanced data visualization techniques. Key Features: ✔ Revenue & Profit Analysis ✔ Financial Performance Monitoring ✔ KPI Tracking & Executive Summary ✔ Regional & Category-Based Analysis ✔ Interactive Filters and Drill-Through Reports ✔ Dynamic Data Modeling & DAX Measures ✔ Professional Data Cleaning & Transformation using Power Query ✔ Advanced Power BI Visualizations eyJrIjoiNWI3OWU3NmQtMjA2Yi00MzY0LTlhNzQtODc5MzUzOWQwMjM2IiwidCI6IjI0MWNlN2VlLTVjYmUtNDczNi1hYWM0LWZkOWZmM2NjMWRkMSIsImMiOjl9Grão Raro: assinaturas de café especial
Relatório de acompanhamento de um clube de assinatura de cafés especiais. A Grão Raro é uma empresa fictícia e todos os dados são fictícios, criados para este portfólio. O relatório foi pensado para quem gerencia a operação de assinaturas: receita, base de assinantes, cancelamentos e entregas, um mês de cada vez, com comparação contra o mês anterior. Visão geral: o resumo do mês. Receita recorrente, assinantes ativos, novas assinaturas e taxa de cancelamento, com a receita por mês e plano, o saldo semanal de entradas e saídas, os cafés mais pedidos, o mapa de assinantes por estado e a preferência de torra. Manual do relatório: o que cada página mostra e como usar cada controle. As páginas Assinantes, Cancelamentos e Pedidos e entregas estão em construção e aparecem no menu com cadeado. Recursos de experiência de uso: Central de ajuda: uma janela de conversa, aberta pelo menu, que simula uma assistente de IA dentro do relatório. A pessoa digita uma pergunta em linguagem natural (“quanto faturou em março?”, “qual café mais vende no Sul?”, “quantos cancelaram por região?”) ou escolhe uma sugestão por assunto, e recebe o número, a comparação, um mini gráfico de barras e, quando cabe, a foto do café. Responde para qualquer mês, por café, torra, região, plano e canal. A conversa é mantida ao trocar de página, e há botões de nova conversa, copiar e avaliar a resposta. Filtros: reunidos em um painel próprio, em botões com estado de hover, com contador de filtros ativos e Limpar filtros. Manual e tour guiado: a página Manual do relatório descreve cada página e cada controle, e o tour “Como usar” apresenta os recursos passo a passo. Acessibilidade: contraste no nível AA em todos os textos, e todos os indicadores batem com os visuais de onde saem.Student Performance Dashboard Project
Student Performance Dashboard This project is an interactive Student Performance Dashboard developed using Microsoft Power BI to analyze and visualize academic performance through dynamic reports and KPIs. Dashboard Features Overall Student Performance Overview Subject-wise Performance Analysis Gender-wise Performance Analysis Interactive KPI Cards Grade Distribution Analysis Top 5 Performing Students Average Percentage & Highest Percentage Section-wise Performance Comparison Dynamic Charts and Summary Tables Tools & Technologies Microsoft Power BI Power Query DAX (Data Analysis Expressions) Data Modeling Key Insights The dashboard enables users to: Monitor overall student performance. Compare subject-wise academic results. Analyze gender-based performance. Identify top-performing students and grade trends. Make data-driven educational decisions through interactive visualizations. I would appreciate your feedback and suggestions to improve this dashboard. Thank you for taking the time to review my project. Developed by: Syed Raahim Ali Course: Data Analytics & BI eyJrIjoiMGM0OWY1MGUtMTk5Yy00MDNmLTllMDItN2EwNWVlNDU3NGU5IiwidCI6IjI0MWNlN2VlLTVjYmUtNDczNi1hYWM0LWZkOWZmM2NjMWRkMSIsImMiOjl9
30 years of Paraguay's exports
Hi, my name is Facundo, and this is my first dashboard. I built it to showcase Paraguay's exports from 1994 to 2025, using data from the Central Bank of Paraguay (BCP). The dashboard lets you explore the total value of exports, the main products, and their destinations. You can also filter by time period (within the 1994–2025 range) and by product. One of the most interesting findings: electricity went from more than half of Paraguay's exports in the 1990s to just 10.9% in 2025, while agriculture now accounts for 64%.
Canada's Hiring Pressure Monitor: where hiring is getting harder, and where talent is overlooked
Is hiring getting harder in Canada, and for whom? This report answers that in five steps, using public Statistics Canada data for July 2026. Market direction: unemployed people per job vacancy fell from 3.1 to 2.9 over the year. Provincial pressure: Alberta and Manitoba had the highest job vacancy rate, at 3.3%. Industry and pay: health care and social assistance added the most payroll jobs (+82,455). Occupation demand: which jobs employers are trying to fill (quarterly). Untapped talent: which groups have lower employment or participation (annual). Every page starts with a question and a finding that updates with your filters. Hovering over any measure explains it, and each page has a colour key. Methodology and sources are on the last page. The data is prepared and checked in Python against the original Statistics Canada files before it reaches Power BI. Built by Pol Smirnov, talent intelligence analyst, Toronto. The live site also has an AI guide: https://polsmirnov.comSingle-Page Power BI Report. Gender Pay imbalances in the UK. 2017-2025 — Analytical Overview
📊UK Gender Pay Gap — Analytical Overview (Single-Page Power BI Report) This is a single-page interactive report to explore gender pay differences in the UK, focusing on pay gaps, bonus eligibility, and gender representation across pay quartiles. 🔍What this report shows: 📉Mean vs median pay gap over time — highlighting structural vs distributional inequality 💰Bonus eligibility by gender and how the gap evolves year-over-year 🧱 Pay quartiles analysis — clear evidence of vertical segregation (“glass ceiling”) 📊Sector comparison — highest and lowest female representation in top pay quartile ⚖️Pay gap severity distribution — most companies remain far from parity 📈Key insight: Even when overall gaps narrow, women remain underrepresented in the top pay quartile, and progress is uneven across sectors. Equality at the bottom does not automatically translate into equality at the top. 🧠 Why a single-page design? All insights live on one canvas — enabling fast comparison, narrative flow, and decision-ready analysis without page switching. 🛠️ Built with: Power BI · DAX · Analytical storytelling 📂Data source: UK Government Gender Pay Gap Service eyJrIjoiN2ZjNmFhMjctMzg0ZS00MjJiLWJmNjUtMjcwZWY1MTZjMWVkIiwidCI6IjY1NWVhZjVhLTBhMTctNDEzOS05NzU5LTFlMDIzMTRkMDJhYiIsImMiOjZ9E-commerce Profitability Audit: Protect, Monitor or Act
Built with the dataset from the ZoomCharts 4U Report Challenge (European e-commerce, 2024–2025). The question: Sales grew 69% and contribution margin more than doubled, but which categories actually stayed profitable? Page 1 – Audit: each category gets a verdict (Protect, Monitor, Act) based on three rules: CM %, product cost % of sales, and other operating cost %. The rules are shown on the page, so the verdict is transparent. Hover over any category to see a tooltip comparing its cost lines with the healthy categories. Page 2 – Detail: a P&L variance table (current vs prior year) and a margin bridge from prior-year CM to current-year CM. Every subtotal reconciles. Key findings: Beauty, Fashion and Sports & Outdoors deliver about 79% of margin. Electronics runs at 3.4% CM, the only category in Act. Product cost rose about $97K, but most costs grew slower than sales, so CM % improved by 3.3 pp. Feedback welcome.E-commerce Profitability Audit: Protect, Monitor or Act
Built with the dataset from the ZoomCharts 4U Report Challenge (European e-commerce, 2024–2025). The question: Sales grew 69% and contribution margin more than doubled, but which categories actually stayed profitable? Page 1 – Audit: each category gets a verdict (Protect, Monitor, Act) based on three rules: CM %, product cost % of sales, and other operating cost %. The rules are shown on the page, so the verdict is transparent. Hover over any category to see a tooltip comparing its cost lines with the healthy categories. Page 2 – Detail: a P&L variance table (current vs prior year) and a margin bridge from prior-year CM to current-year CM. Every subtotal reconciles. Key findings: Beauty, Fashion and Sports & Outdoors deliver about 79% of margin. Electronics runs at 3.4% CM, the only category in Act. Product cost rose about $97K, but most costs grew slower than sales, so CM % improved by 3.3 pp. Feedback welcome.Healthcare Analytics Dashboard - Power BI Template
In hospitals, delays and inefficiencies don’t just cost money, they risk lives. This Healthcare Analytics Dashboard (Power BI Template) turns complex health data into clear, actionable insights that empower teams to act quickly and precisely. Designed from real hospital use cases, this tested solution helps clinical and operational leaders improve care delivery, resource planning, and financial performance. Challenges This Dashboard Solves 1. Unpredictable Length of Stay (LOS) Hospitals often struggle with forecasting patient discharge times. This leads to bottlenecks in bed availability, overcrowded wards, and delayed admissions. Without visibility into LOS trends, resource allocation becomes guesswork. This dashboard provides historical and real-time LOS analysis by department, condition, and demographic, helping hospitals predict and plan bed usage accurately. 2. Hidden Billing Inefficiencies Revenue leakages are common when billing data is scattered or inconsistently tracked across departments. Costs tied to specific treatments, medications, or admission types often go unnoticed. The financial dashboard breaks down billing data by condition, hospital wing, and treatment category, helping analysts pinpoint cost spikes and optimize spend. 3. Readmissions Due to Missed Clinical Patterns Chronic conditions like hypertension can lead to repeated ER visits if treatment effectiveness isn’t monitored. Without automated alerts, early warning signs are often missed. This dashboard flags abnormal blood pressure cases, tracks medication response rates, and alerts clinical teams for timely interventions,reducing readmission risks. 4. Workforce Planning Gaps Admission surges during weekends or specific times of year catch teams unprepared, stretching staff thin. Weekend vs. Weekday admission trends allow administrators to align staffing with actual demand patterns, not assumptions. 5. Manual and Delayed Reporting Relying on Excel sheets and outdated reports can delay crucial decisions and lead to inconsistent care quality. Automated Power BI reports eliminate manual effort, providing stakeholders with real-time, exportable, and easy-to-understand insights. Key Benefits – Why This Dashboard Matters Live Monitoring of Admissions and Bed Utilization Stay on top of patient intake, identify overcrowded departments, and reallocate beds in real time to improve flow and reduce wait times. Length of Stay (LOS) & Discharge Trend Analysis Track average stay durations by department, gender, or diagnosis. Use historical trends to forecast bed demand, avoid overcrowding, and improve discharge efficiency. Hypertension Alerts & Medication Effectiveness Tracking Get automatic alerts for patients with abnormal BP, analyze which treatments work best, and proactively manage chronic conditions to reduce complications. Billing Insights by Condition, Department, and Admission Type Quickly uncover which procedures or admission types generate the highest costs. Identify revenue opportunities or overspending areas across service lines. Demographic-Based Performance Insights Segment KPIs by age group, gender, and medical condition. This helps tailor treatment plans and improve patient outcomes across different populations. Who Needs This? Hospital Admins – Optimize resources and costs Clinical Teams – Enhance treatment protocols Revenue Analysts – Fix billing inefficiencies Healthcare Analysts – Ditch manual reports for live data Data Source: Synthetic healthcare data for illustrative use cases. eyJrIjoiZTQzNDYzYjAtNWI4MC00MDJkLWI0NGEtNTFjMjAyYWU3ZGRiIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9
