TradingView Strategies for 2025: How to Audit the Claims
A list of strategies advertised as the “best of 2025” is only useful if the results can be reconstructed. This article does not establish a verified winner or a live trading record for the named systems in earlier versions of this page. It provides concrete strategy candidates and a way to assess 2025 claims without treating hindsight as evidence available at the time.
Start With the Date of the Decision
Testing a strategy on 2025 data today is retrospective research. It is not evidence that a trader selected those rules before 2025 began. A credible historical claim needs the version of the strategy that existed at the time, its settings, a dated trade record, and the treatment of costs and position sizing.
This distinction matters for “self-optimizing” systems. A table of the best historical parameters still selects from historical results. Whether a person or a script performs that selection, the next evaluation must use later data that did not influence the choice.
Three Strategy Families to Compare
These are unvalidated research candidates, not reconstructions of any proprietary named strategy. The numerical settings define starting experiments and do not carry a performance claim.
1. RSI Recovery With a Trend Filter
A possible long-only specification requires the close to be above a 200-period SMA, then enters when RSI(14) crosses upward through 40 on a completed candle. Submit the entry for the next bar's open. Allow one position at a time. Exit after a completed candle closes below the SMA, using the next available open, or when a protective stop is hit. For this candidate, set that stop two ATR(14) below the actual entry using ATR from the signal bar and keep it fixed.
The hypothesis is that momentum recovery within an upward trend can be useful. The failure mode is a recovery that fails or a trend filter that reacts after a reversal. Compare it with the identical entry and exits without the SMA filter to measure what the filter changes. TradingView's RSI reference explains the underlying oscillator.
2. MACD Crossover With a Fixed Exit Policy
Compare MACD 12/26/9 and 8/24/9 under the same conditions. For each, enter long on a completed-bar upward MACD/signal crossover while flat, and exit on the downward crossover. Model these market orders at the next bar's open. Include the same fixed ATR stop policy as above in both candidates.
Shorter lengths change responsiveness; they do not guarantee entries a fixed number of bars earlier or a higher net return. The ranking can change when trading costs or the sample period changes. Use the official MACD calculation to ensure the two tests differ only in the chosen lengths.
3. Prior-Range Breakout
For a long-only candidate, require the close to exceed the highest high of the previous 20 completed bars, excluding the signal bar from that range. Enter at the next bar's open when flat. Exit after a close below the lowest low of the previous 10 completed bars, again using the next open, or at the fixed initial ATR stop.
This tests continuation beyond a recent range. Its main risks include repeated false breaks, overnight gaps, and fills worse than the breakout price. Including the current bar in the reference high changes the condition and can make a strict close-above-high test impossible.
For all three candidates, specify which order has priority if multiple exits become eligible, how gaps are filled, and what happens at the end of the test. These details belong in the rules before results are compared.
Why Named Strategy Claims Need More Than a Screenshot
Earlier versions of this article attributed results to Trade Ops IQ, Tick Trader IQ, Dead Zone, and Super FVMA combinations without providing reproducible records. Those claims are not used as evidence here. A name alone may refer to different authors, versions, or private settings.
Before evaluating a named system, collect:
| Evidence | Required detail |
|---|---|
| Script identity | Exact publication URL, author, version, and access type |
| Trading rules | Entries, exits, reversals, stop updates, and sizing |
| Test context | Data feed, instrument, dates, session, and chart type |
| Execution | Commission, slippage, leverage, and order timing |
| Results | Full trade list and equity history, including losses |
| Selection history | How many variants were tried before this result was selected |
“Zero lag,” “confluence,” and “AI optimized” describe aspects of a design or its marketing. None supplies the missing execution record. Multiple filters can also select the same underlying price behavior, so their agreement is not automatically independent confirmation.
Build a Chronological Evaluation
Choose a candidate universe and parameter grid before calculating results. If evaluating a claim about 2025, separate any pre-2025 development evidence from results recorded during 2025. When that evidence is unavailable, label the work retrospective.
For a new experiment, use earlier data to choose rules, freeze the selection, and evaluate on a later period. A walk-forward test repeats that sequence: train on an earlier window, apply the selected version only to the next window, then advance. Do not let a later window affect a decision made in an earlier one.
Save each selection, even when it loses money. Report the joined evaluation windows and their individual results; reporting only the best window defeats the purpose.
Execution Can Change the Ranking
Use the same starting capital, exposure limit, cost model, and date range for every comparison. Then repeat with less favorable execution assumptions. If a small increase in costs reverses the ranking, the apparent advantage is fragile.
TradingView's strategy documentation describes the broker emulator, standard versus synthetic charts, and commission and slippage settings. Check the trade list around gaps and ambiguous bars. A smooth equity curve does not resolve uncertainty about which order could have filled first.
Compare net expectancy, drawdown, trade count, and time in market. Where relevant, add a buy-and-hold baseline with comparable capital and costs. A large return can reflect leverage or persistent market exposure rather than better signals.
What AI and Visual Builders Can Contribute
Pineify can help express conditions and orders as Pine Script through its visual strategy workflow. An AI assistant can also help draft an explicit specification or inspect code. Neither process creates a verified track record.
Keep generated logic separate from the evidence used to evaluate it. Check that the implementation uses only information available at the decision time, then compile and test the actual script. Preserve the prompt or specification, script version, settings, and trade export so changes can be traced.
Frequently Asked Questions
Which TradingView strategy was the best in 2025?
This article does not establish a winner. Such a comparison needs a defined universe of strategies, matched risk and costs, and records showing when each version was selected.
Are MACD 8/24/9 settings better than 12/26/9?
They change the indicator's response, but superiority depends on the full strategy and test conditions. Compare them with identical exits and costs, then evaluate the selection on later data.
Does self-optimization prevent overfitting?
No. Automated selection can overfit just as manual selection can. The relevant control is chronological separation between selection data and evaluation data, with all selection decisions recorded.
Can paper trading verify a historical return claim?
It can test prospective behavior under simulated execution, but cannot authenticate a past record. Historical claims need their own dated evidence and a reproducible methodology.