How I realized the screening process that worked for grains broke for softs
I spent six months doing exactly what every trader thinks will save them time: manual screens, spreadsheets stacked like firewood, and alerts copied into Slack. I was screening wheat, corn, and soybeans the old way - weather flags, export inspections, open interest spikes, cash basis moves - and it worked well enough. But then I tried to apply the same checklist to coffee, sugar, and cocoa. That moment changed everything.
It was mid-October. I missed a cluster of coffee rallies because my rules never considered a currency shock in Brazil or a vessel backlog in Santos port. Sugar moved because of a sudden export quota change in Southeast Asia that my daily "crop progress" column ignored. Cocoa rallied after West African labor disputes tightened supply for weeks. The same indicators that gave me early warnings in Chicago futures often generated noise in soft markets. The cost was measurable: in six months I logged 180 hours manually screening softs and executed a handful of trades with a hit rate below 20% and realized drawdowns that made me ask whether I was trading or guessing.
The screening problem: why grain-focused rules fail coffee, sugar, and cocoa
There are two root causes. First, the drivers differ. Wheat, corn, and soybeans lean heavily on planting progress, acreage estimates, and North American export inspections. Softs depend more on harvest logistics, disease outbreaks, currency moves, importing-country demand, and shipment congestion. Second, the data cadence and signal lifetime vary. Grain signals can move over weeks; softs can gap on a single port strike.
What I was doing wrong:
- Applying the same thresholds (eg, 5% carry vs spot) to all markets. That produced a flood of false positives in softs. Using USDA-type weekly reports as the primary driver. For coffee and cocoa, private industry reports and shipping manifests mattered more. Relying on open interest spikes without context. Softs have smaller, more volatile positioning by specs and commercials, so OI moves alone are misleading. Manual, human-only triage. I wasted time cross-checking sources that could be automated, which slowed reaction to fast events.
Rebuilding the screening engine: a commodity-specific, data-first plan
So I stopped trying to make one tool fit all. I rebuilt the screening engine with three principles: commodity-specific inputs, short and long signal windows, and automated triage with human override. The goal was modest and practical - reduce manual screening time by 90% and improve trade selection enough to cut losing trades in half.
Core components I chose:
- Data layers tailored by commodity: satellite NDVI and rainfall indexes for coffee; cane crush and ethanol margins for sugar; port congestion and shipping times for cocoa; USDA and export inspections for grains. Signal windows: intraday alerts for shipment and currency shocks; multi-week signals for weather and crop progress. Scoring engine: a simple weighted score (0-100) per contract combining fundamentals, technical structure (eg, curve shape), and positioning. Scores above 75 generated high-conviction alerts. Cost-efficient stack: Python, Pandas, PostgreSQL, AWS EC2 t3a.medium for cron jobs, db backups to S3, and a lightweight web UI for alerts. Total cloud + data costs ran about $650/month during pilot.
Building the system: a 90-day roadmap from data to live alerts
I broke the work into three 30-day sprints. That kept me honest and gave measurable checkpoints.
Days 1-30: Data ingestion and baseline rules
Tasks completed:
- Cataloged sources and costs. Example: NOAA rainfall datasets (free), Sentinel-2 derived NDVI (free-ish with modest processing cost), IHS Markit vessel tracking (paid, $300/month for limited feed), exchange market data via low-cost API ($50/month). Total first-month outlay: about $1,300 including developer hours. Built ETL pipelines to ingest CSV/API/FTP feeds, store raw tables in PostgreSQL, and normalize time series. I used daily cron jobs to pull updates and incremental loads for large satellite tiles. Implemented baseline filters: exclude markets with <10 contracts average daily volume, flag >1.5x average export inspections for grains, flag currency moves >2% in BRL or CNY for coffee and cocoa respectively.
Days 31-60: Feature engineering and scoring model
Tasks completed:
- Engineered 28 features across markets: carry/backwardation, 14-day realized vol, net spec position change, export inspections surprise (actual vs consensus), port delay index, NDVI anomaly percentiles, and local currency move against USD. Developed a scoring rubric: fundamentals weighted 45%, positioning 25%, market structure 15%, technical filters 15%. We kept weights interpretable so traders could override them. Built a lightweight web UI to display top-10 daily scores, with links to raw data, charts, and recommended watch actions.
Days 61-90: Backtest, calibration, and live paper trading
Tasks completed:
- Backtested rules over 3 years of history where available. For softs, we used a 5-year window to capture cyclical moves. Backtest gave a baseline: high-score alerts (score >75) historically produced positive moves within 14 trading days in 62% of cases for coffee and 68% for cocoa. Ran a 90-day live paper trade. Execution rules: trade only on score >80, max 2% account risk per trade, trailing stop 1.5x ATR, and target 2.5x risk. The paper run had 18 executed trades across softs and grains. Calibrated thresholds: lowered sugar score threshold to 72 because its volatility and microstructure produced delayed moves rather than immediate gaps.
From 6 months of manual screening to 2 hours a week: measurable results in 6 months
Numbers are what mattered. Here's the hard data after six months of live operation, combining paper and real small-size trades to validate the barchart.com engine.
Metric Before (manual) After (automated) Average weekly screening hours 30 hours 2 hours Monthly actionable alerts 4 22 High-conviction hit rate (net move in favor within 14 days) 18% 56% Average trade return per executed trade +1.2% +3.9% Worst drawdown in 6 months -14.5% -5.8% Monthly cost (data + cloud) ~$0 (time cost not billed) $650Concrete outcomes I care about: screening time dropped from ~120 hours per month to under 8. The high-conviction hit rate tripled. Risk-adjusted performance improved: our small live allocation returned 18.4% over six months with a Sharpe of about 1.05 on the limited sample - not earth-shattering, but far cleaner than the previous messy equity curve.
Five screening lessons every commodity trader should know
Here are the lessons the industry glossed over while I burned six months on spreadsheets.
One size does not fit all. Build commodity-specific features and thresholds. Wheat reacts to US weather and USDA estimates; coffee reacts to Brazilian currency, shipping, and disease. Time horizon matters. Shipments and port problems create fast moves in softs. Have intraday monitors for logistics and daily monitors for crop health. Positioning without context is misleading. Open interest spikes need to be cross-checked with COT and exchange flows. A spec unwind in cocoa can look like a rally in coffee if you don't separate the two. Automate the mundane, keep humans for nuance. Let scripts triage and score, but require human sign-off for trades above your risk limit. Measure costs honestly. Paying $650/month for reliable data and 8 hours saved per week is a bargain compared with human time at $200/hour.How you can replicate this screening system without breaking the bank
If you want to copy the setup, here is a practical playbook you can follow in 90 days with a lean budget.
Step-by-step checklist
Inventory your data needs. Make a two-column list: free sources (NOAA, exchange CSVs, Sentinel) and paid feeds (vessel tracking, private crop reports). Budget $400-1,000/month depending on feeds. Build simple ETL. Use Python scripts to pull and normalize data. Store everything in PostgreSQL. Aim for incremental updates and simple schemas. Create 10-15 core features per commodity. Example features for coffee: 14-day BRL change, NDVI anomaly for Minas Gerais, container rates into US east coast, spec net position delta. Design a scoring rule: keep weights simple and transparent. Example: fundamentals 40%, supply chain 30%, positioning 20%, technical 10%. Backtest conservative rules and run a 3-month paper trade. Track hit rate, average return, drawdown, and alert volume. Implement a small real allocation only after paper results improve your baseline. Use strict risk per trade and a maximum portfolio concentration rule.Practical risk rules I use
- Max 2% risk per trade, measured by stop loss size and not notional. Max 12% portfolio concentration in softs at any time. Never scale into trades during initial 48 hours after a high-impact report - let the first move settle unless your signal specifically targets the report.
A short quiz: is your screening setup costing you time or money?
Score each item: Yes = 1, No = 0. Add up the score.
Do you use commodity-specific features rather than a single checklist for all markets? Do you automatically ingest at least three objective data sources daily? Do you have a numerical scoring system for alerts that you can backtest? Do you limit manual screening to under 10 hours per week? Do you track hit rate and average trade return for your alerts?Score interpretation:
- 0-1: Your system is burning time. Stop adding alerts; start automating data collection. 2-3: You have pieces that work. Prioritize a scoring rubric and a short paper trading phase. 4-5: You probably already save time. Focus on refining thresholds and adding one high-quality data feed that addresses your blind spot.
Final thoughts from someone who wasted six months so you don't have to
If you trade commodities, you will waste time. The question is whether you waste it proving old assumptions or building something that scales. Manual screening sounds noble until you see the opportunity cost - missed moves, emotional fatigue, and a spreadsheet that slowly morphs into a cursed relic.
Start small: pick one soft commodity, identify the three most reliable data signals for it, automate their ingestion, and build a simple score. Run a short paper trial, measure honestly, and then scale. The result for me was not some magic system that prints money. It was cleaner decision-making, a steadier PnL curve, and roughly 110 hours a month back in my life.
If you want the specific feature list and scoring template I used for coffee and cocoa, say the word and I will send the CSV of features, weightings, and the SQL schema for the database. No hype, just the hard-won stuff that saved me six months of needless labor.