Crude Oil Trading Strategy

Custom crude oil swing strategy trading on specific weekdays. Learn to use volatility calculations for entry and backtested win rate / average gain per trade.

Published · Updated · Methodology: Technical Indicators

Part of: Algorithmic & Automated Trading

  • Methodology: Technical Indicators
  • Content type: strategy
  • Timeframes: Daily
  • Markets: Crude Oil

Source video

Decoded from: Crude Oil Trading Strategy (Backtest + Setup) by Quantified Strategies — watch the original

Key timestamps:

  • 0:19 - Introduction to crude oil market
  • 0:25 - Strategy rules introduction
  • 0:28 - Rule 1: Calculate 25-day average of daily High minus Low
  • 0:34 - Rule 2: Must be Tuesday or Thursday
  • 0:39 - Backtest results: 341 trades, 0.25% average gain, 58% win rate

Strategy overview

Crude oil is one of the few markets where a daily-bar rule set has to survive not just the market but the contract behind it. This entry decodes "Crude Oil Trading Strategy (Backtest + Setup)" from Quantified Strategies — a channel that presents rule sets and their historical results together, which the title itself signals by putting "Backtest" before "Setup". The index runs about twenty seconds end to end, and its structure is worth noting: the conditions come first, the results last. What is visible in that ordering is that the setup is not an indicator overlay at all, despite the technical-indicator framing — it pairs a range-based volatility yardstick with a day-of-week condition, which makes the entry logic part calendar and part volatility, not a signal line crossing anything.

The closing marker reports 341 trades, a 58% win rate and a 0.25% average gain. Those three numbers do less work together than they appear to. A win rate says nothing without the payoff ratio beside it, and a 0.25% average gain is precisely the magnitude that crude oil's frictions are large enough to erase — which is why the unstated variables matter more than the stated ones: the period covered, and above all the instrument. In crude, "the price" is genuinely ambiguous. Front-month futures, a back-adjusted continuous series, a CFD, and an oil ETF each produce different daily bars, different gaps at roll, and different fills, so the same rule set can return a different average gain on each without anyone having changed a rule.

The day-of-week component carries its own caveat. Conditioning on the calendar rather than on price makes this a seasonality claim, and seasonality claims are where sample size quietly shrinks: the number that constrains the result is the trade count, not the number of bars in the history, and picking specific weekdays out of five is the textbook setting for a pattern that fits the sample better than the market. None of this makes the idea wrong — it makes it a hypothesis worth testing on your own data and your own instrument. No rule set was extracted into this entry, so this page covers the concept and the source video rather than a decoded breakdown; the original video remains the reference for how the conditions are defined.

Topics

crude oil trading strategy · trading strategy · swing trading · daily trading strategy · technical indicators · tradingview strategy · pine script · wti crude oil · commodities trading

Frequently asked questions

What is a daily-timeframe crude oil trading strategy?

It is a rules-based approach that evaluates crude oil once per day, on the close of each daily bar, rather than intraday. Working on daily data reduces the number of decisions and the sensitivity to execution timing, but it also produces far fewer trades per year, which makes the historical sample smaller than intraday testing on the same period.

Does a day-of-week filter make something a seasonality strategy?

Effectively, yes. A condition based on which weekday it is conditions on the calendar rather than on price behaviour, which places it in the seasonality family regardless of what other filters sit alongside it. The main risk with weekday effects is selection: with only five candidates, a subset will always look better in-sample, so out-of-sample and out-of-market checks matter more than usual.

What does a 58% win rate tell you about a strategy?

On its own, very little. A win rate is only interpretable next to the average win versus the average loss, the trade count behind it, and the cost assumptions used. A high win rate paired with a small average gain per trade can still be unprofitable once spread, commission and slippage are applied, while a lower win rate with a larger payoff ratio can be robust.

Why is backtesting crude oil harder than backtesting a stock?

Because crude is traded through expiring futures contracts, so a continuous price history has to be constructed by rolling and adjusting between contracts, and different adjustment methods produce different returns for the same rules. The tradable instrument also changes the result — futures, CFDs and oil ETFs each track the underlying differently. Strategy Decoder catalogs strategies presented in video sources; this entry has no extracted rule set, so use the original video as the reference before testing anything on your own data.

About this strategy page

This trading strategy was decoded by Strategy Decoder's AI from a public YouTube trading video and turned into a structured, reviewable specification. In the interactive app this page shows the full entry and exit logic, risk management settings, the indicators involved with their parameters, AlgoWizard-compatible logic and a Pine Script export ready for TradingView backtesting — plus an automated backtest verdict when one has been computed for this strategy.

Strategy Decoder catalogs 2,229 decoded strategies. Each one is extracted with confidence scoring, cross-linked to the indicators it uses, and kept up to date as new videos are processed daily. Load this page with JavaScript enabled to use the interactive tools, or start from the strategy explorer to filter by methodology, market and timeframe.

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