Data-Driven Swing Trading

Swing trade S&P500, Nasdaq, Futures, and Forex using a data-driven approach. Analyze historical monthly performance for high-probability entries on daily and we

Published · Updated · Methodology: Technical Indicators

Part of: Swing Trading

  • Methodology: Technical Indicators
  • Content type: educational
  • Timeframes: Monthly, Daily, Weekly, Hourly
  • Markets: S&P500, Nasdaq, Futures, Forex

Source video

Decoded from: Swing trade basado en datos ( +500 ticks ) by JOTAGEPEME — watch the original

Key timestamps:

  • 0:12 - Introduction to data-driven trading
  • 0:40 - Decision to buy S&P500 at historical highs based on data
  • 1:25 - Explanation of data analysis for monthly statistics
  • 2:00 - Structure of data files (columns, rows, delimiters)
  • 2:30 - Custom script for data extraction
  • 3:40 - Modeling and segmenting data (averages, deviations)
  • 4:00 - Monthly performance analysis for S&P500
  • 4:30 - July identified as a historically strong month for S&P500
  • 5:00 - Statistical probability of July closing positive
  • 5:20 - Average return for July
  • 5:40 - Statistics on maximum upside and downside for July

Strategy overview

Swing trading holds positions for days to weeks, long enough for a directional move to develop but short enough to stay off the long-term investor's timescale. What separates this entry from most swing-trading material is where the decision comes from: not a chart pattern or an indicator crossover, but a dataset the trader assembles and interrogates before opening the platform. The source video, JOTAGEPEME's Spanish-language "Swing trade basado en datos ( +500 ticks )", spends most of its runtime on that upstream work rather than on the trade itself.

The walkthrough is unusually mechanical for the genre. It moves from raw price files — how the columns and rows are laid out, which delimiters separate the fields — to a custom script written to pull the relevant series out, and then to segmenting that series into monthly statistics: averages and deviations grouped so that a given month can be compared against its own history. The trade under discussion is a long on the S&P 500 while the index sits at historical highs, which is precisely the situation where intuition and base rates tend to disagree. Framing that as a data question is the whole point of the video; the monthly scale is where the statistics live, while the shorter timeframes on this page are where a position built on them would actually be managed.

No rule set was extracted from this source, and that reflects what the video is: a demonstration of a research process rather than a codified system. There is no entry trigger to reproduce, because the output of the workflow is a probability read that informs a discretionary decision. The tick figure in the title is the creator's own framing of the trade discussed, not a tested track record — the transferable part is the pipeline, and anyone rebuilding it would need their own data files, their own extraction step, and their own segmentation choices.

Topics

data-driven trading · swing trading · s&p500 trading strategy · nasdaq strategy · futures trading strategy · forex strategy · monthly trading · daily trading · technical analysis · trading strategy · tradingview strategy · pine script

Frequently asked questions

What is data-driven swing trading?

It is swing trading where the decision to enter is grounded in historical statistics computed from raw price data — such as how an instrument has behaved in a given month across many years — rather than in a chart pattern or indicator signal alone. The analysis happens before the chart is opened.

Why would monthly statistics matter for a swing trade held for days?

Monthly data gives the base rate: the broad tendency and dispersion a period has shown historically. The video uses that longer scale as context for direction, while the actual position would be managed on shorter timeframes — the statistics inform the bias, not the timing.

What does the video actually show you how to do?

It walks through the data-handling side: the structure of the price files (columns, rows, delimiters), a custom script for extracting the relevant series, and then modeling that series into averages and deviations segmented by period. The trading conclusion is presented as the output of that process.

Can this approach be turned into an automated strategy?

Not directly from this source — no mechanical rule set was extracted, because the workflow produces a statistical read that a human then acts on. Strategy Decoder catalogs entries like this one so you can see which video sources contain reproducible rules and which teach a research method instead.

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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