Timeframes & Data

Timeframes & Data

Every backtest runs on a series of candles -- each one representing a slice of price action over a fixed interval. The interval you choose (1 minute, 1 hour, 1 day, etc.) changes everything about your results: how many candles the engine processes, how many trades your strategy takes, and whether the final numbers are statistically meaningful or just noise.

This article explains how timeframe choice shapes your backtest and what data is available across stocks, forex, and crypto.

What Is a Timeframe?

A timeframe is the duration that each candle represents. A 1-hour candle bundles all the price action within that hour into a single open, high, low, and close. A daily candle does the same for an entire trading day.

Simula supports seven timeframes: 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, 4 hours, and 1 day. All seven are available for forex and crypto. Every stock has daily data; a curated set of 193 US symbols, mainly members of the S&P 100 and the Nasdaq-100 plus popular ETFs, also has intraday data, and the timeframe selector shows you which are available for the symbol you are looking at.

Think of it like adjusting the zoom on a camera. A 1-minute chart shows every tiny price fluctuation throughout the day. A daily chart compresses all of that into a single candle. The underlying price movement is identical -- you are just viewing it at different levels of detail.

Why Timeframe Matters

Your timeframe determines how many candles the simulation engine evaluates. More candles means more opportunities for your entry and exit conditions to trigger. This has three direct consequences.

Signal Frequency

The engine checks your conditions once per candle. On a daily chart, it checks once per day. On a 1-hour chart, it checks once per hour -- 24 times more often during a forex trading day. On a 1-minute chart, it checks once per minute -- 60 times more than the hourly chart.

More checks means more chances for your conditions to be met. A moving average crossover that happens three times on a daily chart over a year might happen dozens of times on an hourly chart, because the shorter candles capture smaller, more frequent crossovers that the daily candles smooth over entirely.

Trade Count

More signals generally means more trades. This matters because trade count is the foundation of statistical significance. A backtest that produces 5 trades tells you almost nothing reliable. A backtest that produces 200 trades gives the numbers enough weight to distinguish between a genuine edge and random luck.

If your strategy is producing too few trades on a daily chart, switching to a shorter timeframe is one of the most direct ways to increase your sample size without changing the strategy logic itself.

Data Volume

The number of candles scales dramatically with timeframe. For a forex pair, one year of daily data is roughly 260 candles. One year of hourly data is roughly 6,240 candles. One year of 1-minute data is over 370,000 candles.

More data gives indicators more values to work with and gives the engine a higher-resolution view of price action. But it also means the backtest takes longer to run, since the engine processes every candle one by one.

Data Coverage

Simula provides historical data across three asset classes, each with different coverage and trading hours.

Forex

19 major and cross pairs (EURUSD, USDJPY, GBPUSD, and others). Forex markets trade 24 hours a day, 5 days a week -- from Monday morning in Asia to Friday evening in New York. All seven timeframes are available: 1 minute through daily.

Because forex trades around the clock on weekdays, a single trading day produces 24 hourly candles or 1,440 one-minute candles. This makes forex especially well-suited for shorter timeframes where you need a large data volume.

Crypto

18 cryptocurrency pairs (BTCUSD, ETHUSD, SOLUSD, and others). Crypto markets never close -- they trade 24 hours a day, 7 days a week, including weekends and holidays. All seven timeframes are available.

The 7-day trading week means crypto produces roughly 40% more candles than forex over the same calendar period. One year of hourly crypto data is about 8,760 candles compared to roughly 6,240 for forex.

Stocks

Thousands of US symbols listed on NYSE, NASDAQ, and CBOE, all with daily data. Intraday data covers a smaller, curated set of 193 symbols: mainly members of the S&P 100 and the Nasdaq-100, plus popular ETFs.

Stock markets have fixed trading hours (for US markets, 9:30 AM to 4:00 PM Eastern, Monday through Friday). This means roughly 252 trading days per year. Weekends, holidays, and after-hours sessions are not included, so an intraday stock chart carries about 390 one-minute candles per day, against 1,440 for forex.

Choosing a Timeframe

There is no single best timeframe. The right choice depends on what you are testing and what you need from the results.

Match Your Strategy to the Timeframe

A strategy designed to capture multi-day trends should be tested on daily or 4-hour candles. Testing it on 1-minute candles will not make it better -- it will just generate noise. The 1-minute chart will show constant small crossovers and fluctuations that are irrelevant to a strategy looking for multi-day moves.

Conversely, a strategy designed to capture quick intraday reversals needs a short timeframe to see those reversals at all. On a daily chart, the intraday reversal is invisible -- it happened and resolved within a single candle.

Check Your Trade Count

After running a backtest, look at the trade count. If the strategy produced fewer than 30 trades, the results are not reliable enough to draw conclusions from. Switching to a shorter timeframe or extending the date range are the two simplest ways to increase your sample size.

Consider Trading Costs

Shorter timeframes produce more trades, and more trades means paying spreads, commissions, and slippage more often. A strategy that is profitable on a daily chart can become unprofitable on a 1-minute chart if the per-trade profit is too small to cover the per-trade cost. Always check how sensitive your results are to cost settings when using shorter timeframes.

Date Range and Historical Depth

Beyond timeframe, the date range you select determines how much history the backtest covers. A longer date range exposes your strategy to more market conditions -- bull runs, corrections, sideways periods -- which gives you a more realistic picture of how it performs across different market regimes.

A short date range is not automatically bad, but it limits what you can learn. A one-year backtest during a strong bull market tells you how the strategy performs when prices are rising. It says nothing about what happens during a crash or a year of sideways chop. The more history you include, the more varied the conditions, and the harder it is for the strategy to succeed by accident.

Using More Than One Timeframe

So far this article assumes one timeframe for the whole strategy. You can also let a single condition read a higher timeframe than the chart -- for example, trade on the 1-hour chart but require the 4-hour trend to agree. This is multi-timeframe analysis, and it does not change which candle interval the strategy acts on: the strategy still evaluates at the fastest timeframe it uses, and the higher timeframe is an extra check at each of those candles.

Adding a daily filter to a 1-hour strategy does not slow it to once a day -- it acts on 1-hour candles where the daily filter also passes, so it stays a 1-hour strategy with fewer trades.

Common Mistakes

Mismatching Indicator Periods and Timeframe

A 200-period moving average on a 1-minute chart looks back 200 minutes -- about 3.3 hours. That is very different from a 200-period moving average on a daily chart, which looks back roughly 10 months. If you switch timeframes without adjusting your indicator periods, the indicators are measuring completely different time horizons than you intended.

Comparing Results Across Timeframes

A strategy that returns 50% on a 1-minute chart with 2,000 trades and a strategy that returns 50% on a daily chart with 40 trades are not equivalent. The first result is backed by a large sample. The second is much more likely to be driven by a few lucky trades. When comparing strategies, always look at the trade count and risk-adjusted metrics alongside the raw return.

Ignoring Costs on Short Timeframes

This is the most common trap with lower timeframes. A 1-minute strategy that takes 1,000 trades looks impressive until you factor in the spread paid on every single one. Run the backtest with realistic cost settings before drawing any conclusions.

Where to Go Next

Statistical Significance & Trade Count -- how many trades you need for the results to be meaningful.

Entry & Exit Conditions -- how the signals that produce those trades are defined.

Multi-Timeframe Analysis -- letting one condition read a higher timeframe than the chart it trades on.

Spreads, Commissions & Slippage -- the trading costs that shorter timeframes amplify.

What Is a Backtest? -- the fundamentals of how backtesting works.