Bitcoin is among the most generally mentioned monetary property of the fashionable period. Since its inception, it has developed from a distinct segment digital experiment right into a globally acknowledged funding instrument with institutional adoption and billions in each day buying and selling quantity. Regardless of its inherent volatility, Bitcoin has demonstrated a powerful long-term development trajectory, making it a horny candidate for trend-based and momentum-oriented buying and selling methods. As a decentralized, extremely liquid, and 24/7 traded asset, it presents distinctive alternatives for systematic merchants to discover algorithmic and technical approaches throughout a number of timeframes.
Technical evaluation stays probably the most fashionable strategies for understanding and capitalizing on Bitcoin’s value conduct. Merchants generally depend on instruments resembling Shifting Averages, MACD, Pivot Factors, and Worth Motion ideas to determine developments, reversals, and momentum shifts. These indicators assist translate Bitcoin’s advanced market dynamics into structured, quantifiable alerts 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 aim of this examine is to exhibit a logical, step-by-step strategy of constructing a scientific buying and selling technique for Bitcoin. As an alternative of presenting a single optimized mannequin, we give attention to the gradual growth 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 fairly than information mining or overfitting, making certain that the technique stays each interpretable and replicable.
We consider a long-only strategy, 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 sensible, growth-oriented Bitcoin mannequin that captures medium-term developments whereas managing draw back threat, illustrating how systematic enhancements can improve each stability and efficiency over time.
The dataset is sourced from Gemini Trade, which gives correct and dependable historic Bitcoin/USD value information. Two granularities are used: hourly (1H) information for intraday sign testing and each day (1D) information for higher-timeframe pattern identification (e.g., D1H1 filter).
The evaluation begins in December 2018, comparable to the launch of CME Bitcoin Futures, the second Bitcoin turned broadly accessible to institutional merchants on regulated markets. Knowledge previous to 2018 are excluded, as they characterize a structurally completely different and fewer mature market setting that might be troublesome to duplicate as we speak.
The next chart exhibits the Bitcoin Purchase & Maintain fairness curve from December 2018 to November 2025. Over this era, Bitcoin achieved a formidable common annual return of over 60%, confirming its nature as a high-growth asset. Nonetheless, this efficiency got here at the price of excessive volatility, with a most drawdown of almost –80%, highlighting the large threat inherent in passive publicity to BTC.
Whereas the long-term development potential is plain, the depth and length of historic drawdowns emphasize the necessity for systematic methods and threat administration frameworks to stabilize returns and shield capital.
📈 Determine: Bitcoin Fairness Curve – Purchase & Maintain Technique
Our aim is to design a trend-following technique for Bitcoin utilizing hourly (1H) value information. Pattern-following fashions are effectively suited to unstable property like BTC, as they goal to seize medium-term directional strikes whereas filtering out noise.
As a basis, we make use of the usual MACD indicator, probably the most established and broadly used instruments in technical evaluation. The MACD (Shifting Common Convergence Divergence) is calculated because the distinction between two exponential transferring averages (sometimes 12 and 26 intervals) and generates buying and selling alerts primarily based 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 beneath the sign line.Timeframe: Hourly (1H).
This preliminary mannequin serves as the place to begin, a baseline to guage uncooked indicator conduct 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 principles are easy: go lengthy when the MACD line crosses above the sign line and shut the place when it crosses beneath. 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 developments on Bitcoin.
The technique executes a really excessive variety of trades (2,262), reflecting the noisy nature of hourly value motion. Regardless of its exercise, the outcomes usually are 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 pattern seize. The utmost drawdown of –23.9% confirms that, whereas threat is average in comparison with Purchase & Maintain, there is no such thing as a vital edge.
In abstract, this baseline check exhibits that the uncooked MACD sign on the 1H chart lacks selectivity and requires additional refinement, resembling pattern affirmation from increased timeframes or improved exit administration, to attain significant efficiency enhancements.
The pure MACD strategy clearly lacks construction because it reacts to each small fluctuation on the hourly chart, producing many trades with out delivering constant outcomes. Subsequently, the subsequent logical step is to scale back noise and enhance sign high quality fairly than forcing optimization on parameters.
We are able to obtain this by introducing multi-timeframe affirmation. Specifically, we apply the traditional “Elder precept,” which states that one ought to commerce solely within the course of the dominant pattern 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 screen System, which turned a cornerstone {of professional} technical evaluation.
The precept is easy but highly effective:“Have a look at a better timeframe to determine the principle pattern, after which change to a decrease timeframe to seek out exact entries in its course.”
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 pattern is confirmed, we transfer to the Hourly (H1) chart and take trades solely within the course of that each day pattern.
By including a Day by day (D1) pattern filter to our Hourly (H1) MACD entries, we align short-term alerts with the broader market course.
This adjustment ought to considerably scale back false alerts, lower commerce frequency, and enhance each Sharpe and Calmar ratios. Within the following step, we implement this D1H1 filter and look at 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 course of the upper timeframe pattern.” 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 pattern 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 beneath its sign line, the market is in a downtrend.On the Hourly (H1) timeframe, take trades solely within the course of the D1 pattern.If D1 exhibits an uptrend, execute lengthy entries solely when the H1 MACD crosses upward.Ignore quick alerts completely.
This easy addition removes counter-trend trades and focuses the technique on high-probability setups aligned with the prevailing each day course. In consequence, 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, give attention to the strongest market phases, and commerce solely when each timeframes are synchronized.
On this model, the holding interval stays one bar, that 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 course of the dominant each day pattern, which considerably reduces noise and eliminates most counter-trend setups.
In consequence, 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 ability of upper timeframe affirmation. It’s a easy, logical enhancement that filters out poor market situations and focuses the mannequin on stronger, trend-aligned entries.
After enhancing the entry logic with the D1H1 filter, the subsequent pure step is to reinforce the exit mechanism. The present model closes trades after holding for a hard and fast one bar, which is easy however usually inefficient. Sturdy developments might proceed for a number of hours, but the mannequin exits too early and leaves a good portion of potential revenue on the desk.
To handle this, we introduce a primary trailing cease logic, probably the most intuitive methods to enhance exits with out overcomplicating the system. In our case, the rule is easy. After coming into a protracted place, we proceed holding so long as hourly bars stay optimistic, that means every candle closes increased than it opens. The place is closed on the shut of the primary detrimental bar, signaling the primary potential signal of short-term weak spot.
This easy trailing exit permits the technique to seize extra of the continued pattern whereas mechanically chopping off flat or reversal intervals. It isn’t primarily based on optimization or indicators, solely on value conduct itself, which makes it each clear and strong.
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 aim 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 check of the indicator itself. The outcomes confirmed excessive commerce frequency and weak profitability, proving that uncooked alerts from a single timeframe are inadequate.
D1H1 Filter (Improved Entries) launched the classical top-down affirmation strategy. By buying and selling solely within the course of the upper each day pattern, we diminished 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 optimistic. This easy trailing exit captured stronger developments and improved each Sharpe (1.07) and Calmar (0.87) ratios.
General, 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 sensible, secure, 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.
Writer: David Mesíček, Junior Quant Analyst, Quantpedia
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