[Data simulation]: DeFi interest rate farming VS Trading strategies (“Sell high, Buy low” variations)

Dear redditors,

In this topic I am providing a follow-up to a “research” (I put that in quotes, because some people might disagree with my methodology or “data”) about whether it is better to manage your crypto assets via secure constant/compound interest rate farming or is it better/reasonable to get involved in “active trading” (e.g. sell at major market peaks and repurchase again – with the entire capital – at bottoms). Original topic, which has prompted this simulation: [**\[Discussion\] Investment strategy: “HODL / Staking / DeFi farm” vs “Buy Low, Sell High”**](

# Abstract:

While famous reddit data research studies such as this ([**Timing the market: The absolute worst vs absolute best vs slow and steady**]( done on traditional investment markets, such as putting your savings in the S&P500 index, has argued that the **DCA strategy** (Dollar Cost Averaging, or simply to invest a regular sum at regular intervals, without worrying about the price to offset price fluctuations) is better than **”Timing the market” strategy** (to try to but an asset always as price bottoms, but without selling it at peaks), there are important reasons why I personally disagree about these DCA studies being 100% applicable to crypto markets. If we compare the Bitcoin and S&P500 index prices (over all-time history, and last 5 years, see charts below), we will notice two things:

1. Bitcoin is highly more volatile than S&P500 index (and traditional stocks). It has many clear “peaks” and “bottoms” which server as good opportunities to buy and sell.
2. S&P500 index is not suitable for “buying high and selling low” (**”Timing the market” strategy**), because in general is quite stable (as it is intended and regulated to be!) and there is little opportunity for playing around with “peaks” and “bottoms”. Therefore, confirming the pro DCA strategy argument in [**Timing the market: The absolute worst vs absolute best vs slow and steady**](

[Chart: Bitcoin price \(absolute values, 2017-2022\) and S&P500 Index \(absolute values, 2017-2022\). Data source: Google Finance, 29 March 2022.](

In this simulation I am going to argue that there are scenarios in which **”Timing the market” strategy** can be sometimes effective for crypto assets (due to their lesser regulation, easier access and therefore high price volatility. I will list different variations of this strategy and I will refer to it as **”Sell high, buy low” strategy** (with different amounts of risk, “timing the market” precision – from realistic to almost perfect) and I will compare it to a strategy which consists of stable asset farming via DeFi (Decentralized finance) services, such as crypto asset lending (for interest), liquidity farming and etc. Please note that mixture of strategies (e.g. 50% “DeFi Farming”, 50% “Sell high, Buy low”) are not included. Rather than that, all strategies are presented in isolation.

# Strategies overview:

● **Strategy 0: HODL:** The asset is held inside a crypto wallet. No interest is gained. Upon market exit, the valuation is simply the market price. Not included in the data simulation, because it’s all clear.

● **Strategy 1: DeFi farming:** As explained already, this strategy consists of accumulating gains on the asset principal via various Decentralized finance methods. There are many different (with regard to reward and risk ratios) DeFi farming methods. For the purpose of this simulation I will consider the case of Bitcoin (BTC) with 18% APR compound interest earned quarterly (meaning, the interest compounds 4 times per a year).

● **Strategy 2: Realistic – 1 x “Sell high, Buy low” @ 10k diff:** This strategy suggests **two trades** per year: one purchase and one sale per period. The trade profit is generated via a **price difference of 10 000 USD**. Probability of practical execution: **Very realistic**.

● **Strategy 3: Realistic – 1 x “Sell high, Buy low” @ 20k diff:** This strategy suggests **two trades** per year: one purchase and one sale per period. The trade profit is generated via a price **difference of 20 000 USD**. Probability of practical execution: **Somewhat realistic**.

● **Strategy 4: Medium – 1 x “Sell high, Buy low” @ 30k diff:** This strategy suggests **two trades** per year: one purchase and one sale per period. The trade profit is generated via a price **difference of 30 000 USD**. This strategy assumes the same “bottom” purchase price as Strategy 2 and Strategy 3, but the “peak” sell price is 30 000 USD above “bottom” purchase price. Therefore, this Strategy assumes “high price volatility”. Probability of practical execution: **Medium to Hard**.

● **Strategy 5: Medium – 1 x “Sell lower, Buy lower” @ 30k diff:** This strategy is quite interesting. It’s the same as **Strategy 4: Medium – 1 x “Sell high, Buy low” @ 30k diff**, but the difference is that both the purchase and sell trades are executed at 10 000 USD lower than the previous strategy. This strategy tries to simulate the behavior of wary investors who “sell early” (perhaps even before the all-time-high), but then also buy a lower “bottom”. In essence, it tries to visualize why it is important to purchase an asset at a lower price, than focusing on selling on a higher price. Probability of practical execution: **Hard**.

● **Strategy 6: Aggressive – 2 x “Sell high, Buy low” @ 20k diff:** This strategy suggests **four trades** per year: two purchase and two sale per period. Each trade is executed at a price difference of 20 000 USD. This strategy somewhat highly unlikely market conditions (but which are not totally unrealistic) and suggests very aggressive trading and risk-taking. Probability of practical execution: **Very** **hard**.

# Simulation data used:

● *Initial capital:* All strategies assume that that the simulation starts with asset holding of **1.00 BTC** on **01 January 2022** and **0 USD**.

● *Trades per period:* Some strategies suggest **two purchases** (“BUY”) and **two sales** per one period (one calendar year), while the more “realistic” ones present simulation data only with **one purchase** and **one sale per period**.

● *Period’s end capital:* Each period (year) ends with capital value in the form of fiat money (as each period ends with a sale of Bitcoin for fiat USD), but the estimated coin value is also included for information purposes.

● *Bitcoin average yearly price*: Naive estimations about yearly average BTC price history up to 2032 are made according to this chart\* (*Please see notes in the next section about why I chose not no to follow Plan B’s stock-to-flow price model*):

|Period|Year|Estimated average Bitcoin price (USD)|
|1|2022|60 000|
|2|2023|70 000|
|3|2024|80 000|
|4|2025|90 000|
|5|2026|100 000|
|6|2027|110 000|
|7|2028|120 000|
|8|2029|130 000|
|9|2030|140 000|
|10|2031|150 000|
|11|2032|160 000|

● *Compounding interest note:* I want to make it clear for calculation purposes (from my observations) that the difference between annual compound interest (1 time per year) and quarterly (4 times per year) is not that significant, as we might expect. The interest earned is affected way more than the length of the compounding period (e.g. whether you are earning the interest for 1 or 5 years) and, of course, the interest rate itself. In essence, compound interest guarantees exponential growth of the returns after a certain “interest rate threshold” is passed. For me, personally, the threshold is between 5-10% and anything above that results in exponential gains or losses. See a compound interest graph [here](

# Notes on data used:

● *Bitcoin future average yearly data extrapolation:* Rather than using Plan B’s prediction for 1 000 000 USD per 1 Bitcoin for year 2025, I have used a much more conservative and simpler approach. I took the average monthly price of Bitcoin for the past 8 years (reliable data is available only for the range of 2014-2022) and then I have taken the extrapolation as an average for the future 10 years (up to 2032). I understand that a lot of people will disagree with this simplification. My only defense points are that this price simulations suggest a more conservative price of Bitcoin (as it does not take into effect dollar inflation, and more importantly, the two halvings of Bitcoin in 2024 and 2028, which – historically – always “sky rocket” the price few months after they are introduced). It’s always better to base your future estimations on conservative projects, as anything better than that it always welcome.

[Chart: Bitcoin average yearly price \(2014 – 2022\). Source: Yahoo! Finance.](

[Chart: Bitcoin average yearly price extrapolation for 2022 – 2032 based on average price for 2014 – 2022. Source: Personal contributions.](

*● Stock-to-Flow model not used*: Yes, as you have noticed, this chart does not follow the famous [**Plan B’s Stock-to-Flow model**]( which suggests an exponential growth of Bitcoin price and a price of up to 1 000 000 USD per Bitcoin until year 2025. I don’t do it, for two reasons:

1. First, because Stock-to-Flow model has been greatly challenged during 2021-2022 and there’s simply not enough scientific support (I’m not talking Twitter mentions, but real peer-reviewed science) for the model. I can claim with the same success and verbiage that “my model is identical”. The only difference is that I fully acknowledge that this model is pure simulation. Perhaps a secondary edition of this simulation can be done with this Stock-to-Flow price model.
2. I’m personally realistic about the actual market capitalization needed, in order to make Bitcoin reach 100 000 USD or even more. According [this]( Coindesk analysis ([**The Complete Case for $100K Bitcoin**](), the market cap of Bitcoin needs to reach **9 trillion USD** (2% of current global wealth) or the price of **428 000 USD per Bitcoin** in order to reach the same capitalization as Gold (thus, really becoming *”Digital gold”*). In simpler terms, Bitcoin market capitalization needs to increase 10 times so it can be on equal terms with gold. As of today, 28 March 2022, Bitcoin market cap as is **904 billion USD** and total crypto market cap (the worth of all cryptocurrencies combined) is **2.16 trillion USD** (Source: [Coinmarketcap](

● *Initial purchase price for January 2022:* The purchase price for the first period (January 2022) does not conform the linear growth of the prices for the next periods by \~10 000 USD for the most strategies. If this had to be done, the purchase price must have been 30 000 USD, which meant starting with faulty data from the beginning. I decided to keep 40 000 USD, because this was a relatively stable average price during the first quarter of 2022.

*● Coin valuation in the end of the period:* The last coin valuation (in December 2023) for the majority of the strategies shows no increase in coin value. This is because the actual “coin value increase” is achieved when a purchase (with the accumulated USD capital) is done during the first window opportunity in the next year.

# Simulation:

All strategies – except Strategy 0 – are applied for a 11 year period , from 2022 to 2023. For each strategy all trades are presented in the “Trades” graph for each year. All earnings (or losses) are reinvested immediately in the next period. Google Sheets is used to handle all data.

# Results:

Below are the results from running all 6 strategies with an initial capital of **1.00 Bitcoin** from **01 January 2022** (average Bitcoin price: **40 000 USD**) until **31 December 2032** (extrapolated average Bitcoin price: **160 000 USD**).

**1. Strategy 1: DeFi Farming: Earn constant 18% APR per year**

[Chart: Strategy 1 – DeFi Farming: Investment results.](

**2. Strategy 2: Realistic – 1 x “Sell high, Buy low” @ 10k diff per year**

[Chart: Strategy 2 – Realistic – 1 x \\”Sell high, Buy low\\” @ 10k diff: Investment results](

[Chart: Strategy 2: Realistic – 1 x \\”Sell high, Buy low\\” @ 10k diff: Trade history](

**3. Strategy 3: Realistic – 1 x “Sell high, Buy low” @ 20k diff per year**

[Chart: Strategy 3: Realistic – 1 x \\”Sell high, Buy low\\” @ 20k diff: Investment results](

[Chart: Strategy 3: Realistic – 1 x \\”Sell high, Buy low\\” @ 20k diff: Trade history](

**4. Strategy 4: Realistic – 1 x “Sell high, Buy low” @ 30k diff per year**

[Chart: Strategy 4: Realistic – 1 x \\”Sell high, Buy low\\” @ 30k diff: Investment results](

[Chart: Strategy 4: Realistic – 1 x \\”Sell high, Buy low\\” @ 30k diff: Trade history](

**5. Strategy 5: Medium – 1 x “Sell lower, Buy lower” @ 30k diff per year**

[Chart: Strategy 5: Medium – 1 x \\”Sell lower, Buy lower\\” @ 30k diff: Investment results](

[Chart: Strategy 5: Medium – 1 x \\”Sell lower, Buy lower\\” @ 30k diff: Trade history](

**6. Strategy 6: Aggressive – 2 x “Sell high, Buy low” @ 20k diff per year**

[Chart: Strategy 6: Aggressive – 2 x \\”Sell high, Buy low\\” @ 20k diff: Investment results](

[Chart: Strategy 6: Aggressive – 2 x \\”Sell high, Buy low\\” @ 20k diff: Trade history](

# Interpretations and conclusions:

The most profitable strategy (if all the stars align and executed flawlessly) is **Strategy 6 (Aggressive – 2 x “Sell high, Buy low” @ 20k diff per year),** which is exponentially profitable and relies on active trading (at least 2 cycles of “Sell high, buy low”) per year. In the end this strategy suggests a holding of **39.38 BTC** (**3833% increase**). It suggests nearly 4 times more returns than the second best strategy. There is a high probability of capital loss during trading. Risk rating: **Extreme**.

The second most profitable approach is **Strategy 5 (Medium – 1 x “Sell lower, Buy lower” @ 30k diff per year)**. This strategy gets its profitability from the huge profit margin which it seeks (static 30 000 USD difference between “purchase” and “sell” price) and the fact it tries to aim always at the “lower” version of a “bottom” and “peak” than **Strategy 4**. It provides, slower exponential capital growth. Requires advanced trading expertise, but does not seem “completely impossible” (since it requires execution only twice yearly and keeps the same 30 000 USD margin, while the price increases). High probability of partial capital loss. In the end this strategy suggest a holding of **10.50 BTC** (**950% increase**). Risk rating: **Medium High to High.**

In third place, while mathematically **Strategy 4** (**Realistic – 1 x “Sell high, Buy low” @ 30k diff per year**) is slightly ahead of **Strategy 1** (**DeFi Farming: Earn constant 18% APR per year**) with just **1.04 BTC more** provided as a return (and a total capital increase of **586%** versus **482%**). However, due to the fact that **Strategy 1** is nearly almost safe and requires no trading at all, we have to consider here for the DeFi farming strategy to be the third most optimal (an almost also most profitable strategy too). Also, the chief difference is that while the profitability of **Strategy 4** (and all other strategies except **Strategy 1**) relies entirely on the assumption that either the price will increase, or either – at least – it will keep fluctuating, so trades are made possible, **Strategy 1** does not rely on price at all. Most importantly, as Bitcoin reaches its theoretical market cap it is (theoretically) suggested that the “exponential increase” of the profits (which is observed in the “Sell high, Buy low” strategies) will fade, while the effect of compound interest and its exponential increase will likely take effect exactly at that moment, providing exponential returns to the long-term holders. In the end Strategy 2 (DeFi Farming) **guarantees** a holding of **5.82 BTC** (**482% increase**) without any trading. Risk rating: **Very low to None.**

The penultimate most profitable trading approach is **Strategy 3** (**Realistic – 1 x “Sell high, Buy low” @ 20k diff per year**) which – surprisingly – unlike other active trading strategies **provides only a linear increase on the capital**, due to the fact that every time a trade with a margin of 20 000 USD is executed, as the price increases, the profit margin is actually less (since it’s way more profitable to invest, for example 100 000 USD in Asset A while it’s worth 0.10 USD and sell it at price 0.20 USD, than buying it at 0.90 USD and selling at 1.00 USD). Also this strategy performs worse than the **Strategy 1** (**DeFi Farming**), while still carrying the risks associated with trading. Risk rating: **Medium.**

The worst strategy is **Strategy 2** (**Realistic – 1 x “Sell high, Buy low” @ 10k diff per year**). While, visibly, this strategy suggests a short-term profit while executing a seemingly “profitable” trade with a margin of 10 000 USD, the reality is that as the asset price increases this profit margin is simply a mere illusion. **Most importantly, this strategy leads to actual loss in coin value**. While theoretically – in dollar value – the trader still indeed makes profits, simply holding the asset (and making no trades while) would be even more profitable. This leaves the important conclusion that all trading strategies must have critical profitability thresholds, which if not measured and attained, might result in falsely perceived positive profitability trading.

# Possible future work:

*●* *Recalculate the simulation with price extrapolation which takes into account the two Bitcoin halvings in 2024 and 2028 and modify current price model accordingly.*

*● Recalculate the simulation with on a price extrapolation based on the Stock-to-Flow model.*

# Credits:

All content and images are my original work, except where specified otherwise.

All comments, suggestions and criticism is welcome.

If you liked this study or found it somewhat useful, please do consider upvoting this post. Thank you!

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5 thoughts on “[Data simulation]: DeFi interest rate farming VS Trading strategies (“Sell high, Buy low” variations)”

  1. Very interesting analysis and a strong validation of defi, if correct. That said, you state that the risk of defi farming is very low to none, which seems suspicious given my limited experience in defi and the general community sentiment.

    Can you comment on how you assess the defi farming risk to be that low? Are you think of specific approaches for the 18% APR you’re assuming? What are they?

    Also, is there an alternative model for btc price appreciation? I do like u/nested_dreams suggestion of logarithmic, which would show progressive reduction in rate of appreciation as adoption increases but still a reasonable increase in price versus time over the long term.

  2. I feel like I might have missed, could you highlight how we know what is high and what is low?

    For strategy 2 where the threshold is 10k, is it we sell after a 10k run up and buy when there’s a 10k drawdown?

    Surely this should depend on the price of BTC at the time as at points this would be waiting for a 50% drawdown or it could be 5% drawdown in the future.


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