Seasonal Patterns, Moving Average Strategy

Discover a Soybean futures scalping strategy using seasonal patterns and a moving average filter on 60-minute and daily timeframes. Learn entry/exit rules and A

Published · Updated · Methodology: Technical Indicators

Part of: Moving Average Strategies

  • Methodology: Technical Indicators
  • Content type: strategy
  • Timeframes: 60-minute, Daily
  • Markets: Soybeans (Futures)

Indicators used

  • Moving Average
  • ATR

Source video

Decoded from: Best Intraday Bias/Scalping Strategy Generating $137,381 in profits! by Ali Casey | StatOasis — watch the original

Key timestamps:

  • 0:44 - Seasonal patterns introduction
  • 1:52 - Soybeans heatmap for seasonal patterns
  • 2:24 - First pattern entry/exit conditions
  • 3:45 - Initial strategy performance without filters
  • 4:20 - Introducing Moving Average filter
  • 5:40 - Optimizing entry/exit times for first pattern
  • 7:00 - Second seasonal pattern identified (Monday)
  • 8:00 - Second pattern initial performance
  • 9:00 - Optimizing entry/exit times for second pattern
  • 10:00 - Combining both patterns
  • 11:00 - Adding ATR-based Stop Loss and Take Profit

Strategy overview

A moving average smooths price into a single trend line that traders use to read direction — but in this strategy it plays a supporting role rather than the lead. The primary engine here is seasonality: the tendency of certain markets to move in recurring directions at recurring times, which the video converts into a mechanical intraday directional bias instead of a discretionary hunch.

Decoded from Ali Casey's StatOasis video "Best Intraday Bias/Scalping Strategy Generating $137,381 in profits!", the walkthrough follows the channel's stats-first style. It begins from a seasonal heatmap — soybeans is the worked example — isolates a recurring pattern with defined entry and exit conditions, and measures how that raw calendar edge performs on its own before any filtering. The moving average enters only afterward, layered on top as a screen for the seasonal signals, followed by tuning the specific entry and exit times of day. That ordering is the whole idea: build the seasonal bias first, then let a trend filter and the clock refine it.

The dollar figure in the title is best read as the creator's reported result for one backtest on one market, not a promise — seasonal edges are notoriously sensitive to which years are sampled and can decay as more participants trade them. As with any seasonality-plus-filter approach, what matters is whether the recurring pattern holds out of sample and how the trend filter and volatility-based exits change its behavior across different markets and periods.

Topics

seasonal patterns strategy · moving average strategy · soybean futures strategy · soybeans trading strategy · scalping strategy · intraday strategy · trading strategy · pine script · tradingview strategy · technical indicators · futures trading strategy · 60 minute strategy · daily strategy · atr strategy

Frequently asked questions

How does this strategy combine seasonality with a moving average?

The seasonal pattern is the core signal — a recurring, calendar-based directional tendency turned into an intraday bias. The moving average is added afterward as a filter on top of that seasonal signal, not as the primary entry trigger.

Does the moving average generate the entries in this version?

No. In the video's build, the seasonal pattern defines the entries and exits first; the moving average is introduced later as a filter to screen those seasonal signals, and the entry/exit times are then optimized around it.

What market does the video use as its example?

It works through soybeans, using a seasonal heatmap to surface the recurring pattern before defining the trade conditions. The same seasonality-plus-filter logic is presented as something you would re-test market by market.

How can I evaluate a seasonal strategy like this before trading it?

Backtest the recurring pattern across many years and markets to see whether the edge is stable out of sample rather than a curve-fit to a few seasons. Strategy Decoder extracts the structure of strategies like this from video sources so you can test them on TradingView.

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.

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