2/12 The article first describes why the #Bitcoin price appears to move in 4-year cycles:
- Halvings occur every ~4 years
- Halvings create a supply shock that may drive up the price as described here:
- N=2.25, but price action so far supports the thesis
3/12 The #Bitcoin Price Temperature (BPT) is a metric for the relative distance between the daily price and its 4-year moving average.
More simply put: high BPT values reflect potentially (over)heated prices, whereas low BPT values are a sign of potentially (under)cooled prices.
4/12 With BPT, the relative price levels of previous market cycles are much more comparable.
Based on TA, several levels stand out:
- BPT=8 (red): All previous cycles topped soon after
- BPT=6 (orange): Resistance during bull run
- BPT=2 (green): Support/resistance at key points
5/12 The similarity of the relative price levels during previous cycles becomes even more apparent in this chart. #Bitcoins first period after launch was unique (no price, high inflation), but every halving cycle so far has had a similar boom-bust cycle shortly after the halving.
6/12 It is important to note that the #Bitcoin Price Temperature is solely backwards-looking and has no predictive power.
A good case for the 4-year cycles can be made, but it is also possible to argue against the hypothesis that those halving-induced cycles will keep repeating:
7/12 However, the current (2020~2024) and previous (2016-2020) halving cycles again are very similar (r=0.77) so far.
Clearly no guarantees about the future, but it might be interesting to monitor to what extent future prices will again mimic the previous cycle(s).
8/12 This brings us to the #Bitcoin Price Temperature (BPT) Bands; a visual representation of BPT levels on the #Bitcoin price chart, accompanied by a color-overlay that reflects the price temperature.
Observations: 1) If a BPT is reached again, it tends to be at higher price.
9/12 (continued)
2) If a price level is reached again, the 'temperature' has usually cooled off. E.g., late 2017 ~$20k had a BPT of ~8, whereas late 2020 ~$20k had a BPT of ~3.
3) Like Bollinger Bands, BPT Bands widen during high volatility and contract during low volatility.
10/12 The adaptiveness to volatility marks a key difference between the BPT (Bands) & the Mayer Multiple or other moving average-multiple based indicators. The BPT Bands slope up more steeply towards higher prices during volatility, which may be more appropriate in that context.
11/12 Future efforts could go out to creating a @TradingView indicator for the BPT (potentially using a wider range of Bands, e.g., -2 to 12) or creating a Python implementation that can be more easily be adopted on websites that host #Bitcoin price charts and indicators.
12/12 Special thanks go out to @Anoi30604540 for providing feedback and reviewing the draft of the article! 🙏
I am open for questions, improvement suggestions or anyone looking to further develop or implement this. The used code is available on GitHub:
2/n Just for reference, if you have no idea what this is about and want to read up, this thread might help. If you speak Dutch, the @BitcoinMagNL article in my pinned tweet does the trick as well.
1/n Since @100trillionUSD posted his article, a lot of critical new developments in the #Bitcoin Stock-to-Flow (S2F) modeling unfolded. The discussion is a bit complex & scattered, so I'll attempt to summarize recent events in a (hopefully) easy-to-understand) way.
A thread. 👇
2/n If you'd like a brief history of the evolution of the #Bitcoin S2F model before we dive into the matter at hand, this thread will get you up to speed:
3/n The discussion at hand was spurred by @100trillionUSD's latest article that introduced the 'Bitcoin Stock-to-Flow Cross Asset Model' (S2FX), but the discussion we're about to highlight itself is actually not about that model, but about its predecessors.
1/n Yesterday, @100trillionUSD published his third Medium article, called 'Bitcoin Stock-to-Flow Cross Asset Model', in which he introduces a new iteration of the S2F model that utilizes a new approach to modeling assets based on scarcity:
2/n The first S/F model (March 2019) innovated by creating a power-law model, regressing #Bitcoin's price by its Stock-to-Flow ratio (S2F); it's total supply divided by the new issuance. The underlying idea was that scarcity is driving Bitcoin's price.
3/n The first model used monthly #Bitcoin data and used silver and gold as benchmarks. Both aligned well, albeit suggesting that the modelled price was on the low-end. As @RaoulGMI pointed out; this already indicated that the method might hold merit for cross-asset valuation.
1/n With the #Bitcoin halvening in sight and the $BCH & $BSV halvings happening this past week, I decided to dive into @coinmetrics' on-chain data myself and create some charts to compare their hash rate and fees as a percentage of the block reward over time.
A thread. 👇
2/n Let's start with the one and only Bitcoin; #BTC. $BTC's hash rate has been steadily growing over time. As the charts clearly show, the halvenings (striped vertical lines) definitely didn't create a 'mining death spiral' - the hash rate actually accelerated afterwards.
3/n The most likely explanation for the growth in hash rate after the halvenings is that as #BTC price increased as a result of the Quantitative Hardening (h/t @_Schmiegle), mining became more profitable, attracting new miners and thus hash rate.
1/n Finally read the latest work by @BurgerCryptoAM & @btconometrics. Once again staggering findings, further fortifying the evidence that the relation between #Bitcoin's Stock-to-Flow (S/F) ratio and price (originally discovered by @100trillionUSD) is not spurious.
A thread. 👇
2/n On March 7th, @BurgerCryptoAM published the fourth article in his series on the S/F model for #Bitcoin. The article contributes by suggesting a model selection framework to help determine which statistical test(s) should be used in this process.
3/n The article @BurgerCryptoAM refers to is a 2018 publication in The Journal of Finance and Data Science that introduces a decision tree to help researchers determine which statistical test(s) to use in time series analysis, such as economic data.