Why Historical Volume Data Matters

🔍 Why Past Production Records Matter in Mining Operations ⛏️ Understanding historical production performance is critical for evaluating the health and potential of a mining asset. Over a 16-year period (2009–2024), this operation processed 352.13 million tonnes of ore at an average 0.57% Cu grade — yielding over 1.84 million tonnes of copper with 91.84% average recovery. 📊 What Past Production Tells Us: ✅ Orebody Performance Trends – Head grade evolution reveals ore depletion or variability. – Recovery efficiency reflects metallurgical adaptability. ✅ Operational Optimization – Milling throughput patterns help benchmark plant performance. – Annual production shifts inform asset utilization and downtime analysis. ✅ Forward Planning & Life-of-Mine (LOM) Forecasts – Historical data supports resource-to-reserve conversion assumptions. – Aids in calibrating cut-off grades, mining schedules, and expansion viability. ✅ Risk & Investment Assessment – Year-over-year trends in grade, tonnes, and recovery inform economic robustness. – Identifies inflection points where intervention improved outcomes. 📉 Example Insights from the Dataset: ·       Peak production achieved in Year 2 with >146 kt Cu at 0.86% grade. ·       Metallurgical recovery peaked at ~94.7% in Year 8 — showing process optimization. ·       Recent years show grade softening to ~0.49–0.53%, but recovery remains resilient. 🔧 Takeaway: Past production records are more than just historical numbers — they are strategic tools for validating feasibility studies, guiding process improvements, and building confidence in future project development. #Mining #Geology #Copper #MinePlanning #Metallurgy #ResourceEstimation #FeasibilityStudies #ProductionData #MineOptimization #GeologicalModelling

The third Wyckoff law states that the changes in an asset's price are a result of an effort, which is represented by the trading volume. If the price is in harmony with the volume, there is a good chance the trend will continue. Usual cases (Proportional): -Large Volume, Large Range -Small Volume, Small Range Unusual cases (Divergent): -Large Volume, Small Range -Small Volume, Large Range Volume Spread Analysis (VSA) is a trading methodology that analyzes the relationship between volume and price movements in financial markets. Volume Analysis: VSA focuses on analyzing the trading volume accompanying price movements. Quantitative traders can incorporate volume data into their trading models to gain insights into market dynamics. By studying changes in volume, they can identify periods of accumulation (buying) or distribution (selling) and gauge the strength of market trends. Price-Volume Patterns: VSA identifies specific price and volume patterns that suggest potential market movements. Quantitative traders can develop algorithms that scan historical price and volume data to detect these patterns automatically. For example, they might look for price bar formations with high volume, indicating strong buying or selling pressure. Confirmation and Filters: VSA can act as a confirmation tool for quantitative trading strategies. Traders can use VSA analysis to validate other technical indicators or signals generated by their models. This helps reduce false signals and increases the robustness of the trading strategy. Market State Analysis: VSA can provide insights into the overall market state, such as the presence of institutional buying or selling, accumulation or distribution phases, or the presence of market manipulation. Quantitative traders can use this information to adjust their trading strategies accordingly. Risk Management: VSA can assist in risk management by providing additional information about market sentiment and potential reversals. By incorporating VSA analysis into their risk management models, quantitative traders can dynamically adjust their position sizes, stop-loss levels, or exit strategies based on volume and price movements. It's important to note that the effectiveness of VSA in quantitative trading depends on the quality and accuracy of the volume data being used. Reliable and accurate volume data is crucial for proper analysis and interpretation of VSA signals.

Healthcare is in crisis and it’s only getting worse! Hospitals constantly face fluctuating demand: Staff shortages during peak seasons Overcrowded emergency rooms Wasted resources during low demand periods What if you could predict these patterns in advance and prepare for them? Time Series Models analyze historical data to identify trends and patterns over time — like seasonal spikes or daily fluctuations. ✅ Step 1: Collect Historical Data Gather key data points, including: Patient admissions Emergency visits Staff availability Resource consumption (beds, medication, equipment) ✅ Step 2: Identify Seasonal Patterns The model can uncover hidden trends: Higher ER visits during flu season Increased staffing demand on weekends and holidays Decline in outpatient visits during summer months ✅ Step 3: Predict Future Demand Once patterns are identified, the model can forecast: When patient volume will spike How many staff members will be needed What resources should be stocked up ✅ Step 4: Scale Across Departments The model can be applied to: Emergency rooms ICU Outpatient clinics Pharmacy services The more data it processes, the smarter it gets & continuously improving accuracy. Using Time Series Models can significantly improve patient care. 1) Hospitals can reduce wait times, enable faster diagnosis and treatment, and ultimately enhance patient satisfaction. 2) It also helps with workforce management by reducing staff burnout, balancing workloads across shifts, and minimizing last-minute scheduling issues. 3) From a cost perspective, these models drive greater efficiency by lowering operational costs, reducing waste of medical supplies and ensuring smarter use of hospital resources. Time Series Models are helping hospitals anticipate demand, optimize resources and improve care. #healthcare #it #healthtech #hospital

🤔 What good is BigQuery without historical #GA4 data? One of the core functions of digital analytics is understanding changes over time. You can't see how website traffic, user behaviour, or conversions have changed without historical data. This makes it difficult to identify trends, measure the impact of marketing campaigns, or track progress towards goals. Historical data provides context for understanding current data points. For example, A sudden spike in traffic might be cause for concern, but if you see it happened at the same time last year, it might be a seasonal trend and less alarming. If you have only recently connected GA4 with BigQuery, you may not have all the historical data in your BigQuery project. This is because, by default, the GA4 data is imported to BigQuery only from the date you first connected your GA4 property to your BigQuery project. If you want historical GA4 data in your BigQuery project, you need to backfill GA4 data in BigQuery. For more details, check my comment. 👇