Bitcoin is likely one of the most generally mentioned monetary property of the trendy period. Since its inception, it has advanced from a distinct segment digital experiment right into a globally acknowledged funding instrument with institutional adoption and billions in day by day buying and selling quantity. Regardless of its inherent volatility, Bitcoin has demonstrated a robust long-term development trajectory, making it a sexy candidate for trend-based and momentum-oriented buying and selling methods. As a decentralized, extremely liquid, and 24/7 traded asset, it provides distinctive alternatives for systematic merchants to discover algorithmic and technical approaches throughout a number of timeframes.
Technical evaluation stays some of the in style strategies for understanding and capitalizing on Bitcoin’s worth habits. Merchants generally depend on instruments corresponding to Transferring Averages, MACD, Pivot Factors, and Value Motion rules to determine traits, reversals, and momentum shifts. These indicators assist translate Bitcoin’s advanced market dynamics into structured, quantifiable indicators appropriate for rule-based methods. On this examine, we apply such technical ideas to assemble and refine a trend-following technique for Bitcoin, progressing step-by-step from a easy MACD setup towards an improved multi-timeframe mannequin.
The first purpose of this examine is to exhibit a logical, step-by-step technique of constructing a scientific buying and selling technique for Bitcoin. As a substitute of presenting a single optimized mannequin, we deal with the gradual improvement of a clear, rule-based framework, ranging from a easy indicator setup and progressively refining it via rational enhancements. Every enhancement is guided by clear logic slightly than information mining or overfitting, guaranteeing that the technique stays each interpretable and replicable.
We think about a long-only method, as cryptocurrencies like Bitcoin exhibit a long-term upward bias pushed by adoption, shortage, and macroeconomic components. Lengthy-only methods are additionally extra sensible for retail and institutional buyers, given the complexity and prices related to shorting crypto property. Our goal is subsequently to design a practical, growth-oriented Bitcoin mannequin that captures medium-term traits whereas managing draw back danger, illustrating how systematic enhancements can improve each stability and efficiency over time.
The dataset is sourced from Gemini Change, which offers correct and dependable historic Bitcoin/USD worth data. Two granularities are used: hourly (1H) information for intraday sign testing and day by day (1D) information for higher-timeframe development identification (e.g., D1H1 filter).
The evaluation begins in December 2018, akin to the launch of CME Bitcoin Futures, the second Bitcoin turned broadly accessible to institutional merchants on regulated markets. Information previous to 2018 are excluded, as they characterize a structurally totally different and fewer mature market surroundings that might be tough to duplicate at the moment.
The next chart reveals the Bitcoin Purchase & Maintain fairness curve from December 2018 to November 2025. Over this era, Bitcoin achieved a powerful common annual return of over 60%, confirming its nature as a high-growth asset. Nevertheless, this efficiency got here at the price of excessive volatility, with a most drawdown of practically –80%, highlighting the large danger inherent in passive publicity to BTC.
Whereas the long-term development potential is simple, the depth and length of historic drawdowns emphasize the necessity for systematic methods and danger administration frameworks to stabilize returns and defend capital.
📈 Determine: Bitcoin Fairness Curve – Purchase & Maintain Technique
Our purpose is to design a trend-following technique for Bitcoin utilizing hourly (1H) worth information. Pattern-following fashions are properly fitted to risky property like BTC, as they intention to seize medium-term directional strikes whereas filtering out noise.
As a basis, we make use of the usual MACD indicator, some of the established and broadly used instruments in technical evaluation. The MACD (Transferring Common Convergence Divergence) is calculated because the distinction between two exponential transferring averages (sometimes 12 and 26 intervals) and generates buying and selling indicators based mostly on its crossover with a 9-period sign line.
On this base model, we assemble a easy long-only MACD crossover technique:Purchase (Lengthy) when the MACD line crosses above the sign line.Shut place (Flat) when the MACD line crosses under the sign line.Timeframe: Hourly (1H).
This preliminary mannequin serves as the start line, a baseline to guage uncooked indicator habits earlier than introducing higher-timeframe filters or exit enhancements in subsequent steps.
The primary model of our mannequin is a pure MACD crossover technique on the hourly (1H) timeframe. The foundations are easy: go lengthy when the MACD line crosses above the sign line and shut the place when it crosses under. This model makes use of no filters or exit logic; it merely exams whether or not the MACD indicator alone can determine worthwhile short-term traits on Bitcoin.
The technique executes a really excessive variety of trades (2,262), reflecting the noisy nature of hourly worth motion. Regardless of its exercise, the outcomes should not spectacular. An annual return of solely 4.6%, a Sharpe ratio of 0.33, and a Calmar ratio of 0.19 point out poor effectivity and restricted development seize. The utmost drawdown of –23.9% confirms that, whereas danger is reasonable in comparison with Purchase & Maintain, there isn’t any vital edge.
In abstract, this baseline take a look at reveals that the uncooked MACD sign on the 1H chart lacks selectivity and requires additional refinement, corresponding to development affirmation from increased timeframes or improved exit administration, to attain significant efficiency enhancements.
The pure MACD method clearly lacks construction because it reacts to each small fluctuation on the hourly chart, producing many trades with out delivering constant outcomes. Subsequently, the following logical step is to scale back noise and enhance sign high quality slightly than forcing optimization on parameters.
We will obtain this by introducing multi-timeframe affirmation. Specifically, we apply the traditional “Elder precept,” which states that one ought to commerce solely within the route of the dominant development from a better timeframe. The idea behind this enchancment comes from one of many classics of technical buying and selling, Alexander Elder’s e-book “Come Into My Buying and selling Room.” Elder launched the thought of the Triple Display System, which turned a cornerstone {of professional} technical evaluation.
The precept is straightforward but highly effective:“Have a look at a better timeframe to determine the primary development, after which change to a decrease timeframe to seek out exact entries in its route.”
In our context, this implies checking the Day by day (D1) chart first to find out whether or not Bitcoin is in an uptrend or downtrend. As soon as the dominant development is confirmed, we transfer to the Hourly (H1) chart and take trades solely within the route of that day by day development.
By including a Day by day (D1) development filter to our Hourly (H1) MACD entries, we align short-term indicators with the broader market route.
This adjustment ought to considerably cut back false indicators, lower commerce frequency, and enhance each Sharpe and Calmar ratios. Within the following step, we implement this D1H1 filter and study the way it refines the entry logic and general efficiency of the technique.
To make the technique extra selective in its entries, we introduce a D1H1 multi-timeframe filter impressed by Alexander Elder’s precept: “All the time commerce within the route of the upper timeframe development.” Whereas the bottom MACD mannequin reacts to each small intraday fluctuation, this filter ensures that trades are solely taken when the broader market context helps them.
The logic is as follows:On the Day by day (D1) timeframe, decide the first development utilizing the MACD indicator.If the D1 MACD line is above its sign line, the market is in an uptrend.If the D1 MACD line is under its sign line, the market is in a downtrend.On the Hourly (H1) timeframe, take trades solely within the route of the D1 development.If D1 reveals an uptrend, execute lengthy entries solely when the H1 MACD crosses upward.Ignore quick indicators fully.
This straightforward addition removes counter-trend trades and focuses the technique on high-probability setups aligned with the prevailing day by day route. Consequently, the variety of trades decreases, however the risk-adjusted efficiency improves, sometimes mirrored in a better Sharpe and Calmar ratio and a smoother fairness curve.
This classical top-down logic permits the technique to keep away from counter-trend noise, deal with the strongest market phases, and commerce solely when each timeframes are synchronized.
On this model, the holding interval stays one bar, which means the technique nonetheless opens and closes positions inside a single hourly candle. What modifications, nonetheless, is the standard of entries. Because of the D1H1 filter, the technique now trades solely within the route of the dominant day by day development, which considerably reduces noise and eliminates most counter-trend setups.
Consequently, we see a transparent enchancment in stability and consistency. The variety of trades drops from over 2,200 to round 1,000, however the risk-adjusted metrics enhance noticeably. Annual return rises to six.6% (from 4.6%), most drawdown improves from –23.9% to –12.4%, and the Sharpe ratio will increase from 0.33 to 0.80.
Though the general revenue stays modest, this step demonstrates the facility of upper timeframe affirmation. It’s a easy, logical enhancement that filters out poor market circumstances and focuses the mannequin on stronger, trend-aligned entries.
After bettering the entry logic with the D1H1 filter, the following pure step is to reinforce the exit mechanism. The present model closes trades after holding for a set one bar, which is straightforward however usually inefficient. Sturdy traits could proceed for a number of hours, but the mannequin exits too early and leaves a good portion of potential revenue on the desk.
To deal with this, we introduce a primary trailing cease logic, some of the intuitive methods to enhance exits with out overcomplicating the system. In our case, the rule is easy. After coming into an extended place, we proceed holding so long as hourly bars stay constructive, which means every candle closes increased than it opens. The place is closed on the shut of the primary adverse bar, signaling the primary potential signal of short-term weak point.
This straightforward trailing exit permits the technique to seize extra of the continuing development whereas routinely chopping off flat or reversal intervals. It’s not based mostly on optimization or indicators, solely on worth habits itself, which makes it each clear and sturdy.
Within the subsequent step, we apply this trailing logic to the D1H1 mannequin and consider the way it impacts profitability, volatility, and drawdowns.
All through the examine, our purpose was to develop a Bitcoin trend-following mannequin step-by-step, not via optimization however via logical structural enhancements. Every enhancement was grounded in a transparent rationale and demonstrated measurable progress in stability and effectivity.
MACD Pure (Base Technique) served as a benchmark take a look at of the indicator itself. The outcomes confirmed excessive commerce frequency and weak profitability, proving that uncooked indicators from a single timeframe are inadequate.
D1H1 Filter (Improved Entries) launched the classical top-down affirmation method. By buying and selling solely within the route of the upper day by day development, we lowered noise, improved selectivity, and achieved smoother fairness development.
D1H1 + Trailing Cease (Improved Exits) additional refined the technique by permitting positions to stay open so long as hourly candles stayed constructive. This straightforward trailing exit captured stronger traits and improved each Sharpe (1.07) and Calmar (0.87) ratios.
Total, the evolution from a easy MACD mannequin to a multi-timeframe, price-behavior-aware technique demonstrates a key precept of systematic buying and selling: robustness comes from construction, not complexity. Even with out parameter optimization, every logical layer—increased timeframe filtering and adaptive exits—contributed to a extra life like, steady, and risk-efficient long-only framework for Bitcoin.
The ultimate model, D1H1 STOP, illustrates that self-discipline and logical design can rework a mediocre buying and selling rule right into a constant and replicable quantitative technique appropriate for contemporary crypto markets.
Creator: David Mesíček, Junior Quant Analyst, Quantpedia
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