# What causes bitcoin price fluctuation of agricultural products

Farringdon Capital Management is a Dutch boutique asset manager that has a strong focus on Valuation and Rational investments. The analysis is made in conjunction with their understanding of how the monetary system works. Bitcoin was the first blockchain-based crypto currency and remains the most popular and valuable Coin available. Bitcoin was launched on 3rd of January and as of today there are over However, there are thousands of alternate cryptocurrencies with various functions and specifications.

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What causes bitcoin price fluctuation of agricultural products

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Content:

- Yield farming: An investing strategy involving staking or lending crypto assets to generate returns
- The Use of Control Charts in the Study of Bitcoin’s Price Variability
- Inflation Hit a 40-Year High in December. Here’s What Costs More, and How to Plan for It
- What Determines the Price of Bitcoin?
- RAAX Evolves with Bitcoin
- The rise of using cryptocurrency in business

**WATCH RELATED VIDEO: Why Does Bitcoin Price Fluctuate!? - It's Up, It's Down!**

## Yield farming: An investing strategy involving staking or lending crypto assets to generate returns

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for cryptocurrencies for the period between Nov. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks.

Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market. Today, there are more than actively traded cryptocurrencies.

Between and millions of private as well as institutional investors are in the different transaction networks, according to a recent survey [ 2 ], and access to the market has become easier over time.

Major cryptocurrencies can be bought using fiat currency in a number of online exchanges e. Since , over hedge funds specialised in cryptocurrencies have emerged and Bitcoin futures have been launched to address institutional demand for trading and hedging Bitcoin [ 6 ].

The market is diverse and provides investors with many different products. While this is true on average, various studies have focused on the analysis and forecasting of price fluctuations, using mostly traditional approaches for financial markets analysis and prediction [ 31 — 35 ].

The success of machine learning techniques for stock markets prediction [ 36 — 42 ] suggests that these methods could be effective also in predicting cryptocurrencies prices. However, the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests [ 43 ], Bayesian neural network [ 44 ], long short-term memory neural network [ 45 ], and other algorithms [ 32 , 46 ].

These studies were able to anticipate, to different degrees, the price fluctuations of Bitcoin, and revealed that best results were achieved by neural network based algorithms. Deep reinforcement learning was showed to beat the uniform buy and hold strategy [ 47 ] in predicting the prices of 12 cryptocurrencies over one-year period [ 48 ]. Other attempts to use machine learning to predict the prices of cryptocurrencies other than Bitcoin come from nonacademic sources [ 49 — 54 ].

Most of these analyses focused on a limited number of currencies and did not provide benchmark comparisons for their results. Here, we test the performance of three models in predicting daily cryptocurrency price for 1, currencies.

Two of the models are based on gradient boosting decision trees [ 55 ] and one is based on long short-term memory LSTM recurrent neural networks [ 56 ]. In all cases, we build investment portfolios based on the predictions and we compare their performance in terms of return on investment. The article is structured as follows: In Materials and Methods we describe the data see Data Description and Preprocessing , the metrics characterizing cryptocurrencies that are used along the paper see Metrics , the forecasting algorithms see Forecasting Algorithms , and the evaluation metrics see Evaluation.

In Results, we present and compare the results obtained with the three forecasting algorithms and the baseline method. In Conclusion, we conclude and discuss results. Cryptocurrency data was extracted from the website Coin Market Cap [ 61 ], collecting daily data from exchange markets platforms starting in the period between November 11, , and April 24, The dataset contains the daily price in US dollars, the market capitalization, and the trading volume of cryptocurrencies, where the market capitalization is the product between price and circulating supply, and the volume is the number of coins exchanged in a day.

The daily price is computed as the volume weighted average of all prices reported at each market. Figure 1 shows the number of currencies with trading volume larger than over time, for different values of. In the following sections, we consider that only currencies with daily trading volume higher than USD United States dollar can be traded at any given day.

The website lists cryptocurrencies traded on public exchange markets that have existed for more than 30 days and for which an API and a public URL showing the total mined supply are available. Information on the market capitalization of cryptocurrencies that are not traded in the 6 hours preceding the weekly release of data is not included on the website. Cryptocurrencies inactive for 7 days are not included in the list released.

These measures imply that some cryptocurrencies can disappear from the list to reappear later on. In this case, we consider the price to be the same as before disappearing. However, this choice does not affect results since only in 28 cases the currency has volume higher than USD right before disappearing note that there are , entries in the dataset with volume larger than USD. Cryptocurrencies are characterized over time by several metrics, namely, i Price, the exchange rate, determined by supply and demand dynamics.

The profitability of a currency over time can be quantified through the return on investment ROI , measuring the return of an investment made at day relative to the cost [ 62 ]. The index rolls across days and it is included between 0 and , with November 11, , and April 24, Since we are interested in the short-term performance, we consider the return on investment after 1 day defined as.

In Figure 2 , we show the evolution of the over time for Bitcoin orange line and on average for currencies whose volume is larger than USD at blue line.

In both cases, the average return on investment over the period considered is larger than 0, reflecting the overall growth of the market. We test and compare three supervised methods for short-term price forecasting. The third method is based on the long short-term memory LSTM algorithm for recurrent neural networks [ 56 ] that have demonstrated to achieve state-of-the-art results in time-series forecasting [ 65 ].

Method 1. The first method considers one single regression model to describe the change in price of all currencies see Figure 3. The model is an ensemble of regression trees built by the XGBoost algorithm. The features of the model are characteristics of a currency between time and and the target is the ROI of the currency at time , where is a parameter to be determined.

The characteristics considered for each currency are price, market capitalization, market share, rank, volume, and ROI see 1. The features for the regression are built across the window between and included see Figure 3. Specifically, we consider the average, the standard deviation, the median, the last value, and the trend e.

In the training phase, we include all currencies with volume larger than USD and between and. In general, larger training windows do not necessarily lead to better results see results section , because the market evolves across time. In the prediction phase, we test on the set of existing currencies at day. This procedure is repeated for values of included between January 1, , and April 24, Method 2. Also the second method relies on XGBoost, but now the algorithm is used to build a different regression model for each currency see Figure 4.

The features of the model for currency are the characteristics of all the currencies in the dataset between and included and the target is the ROI of at day i.

The features of the model are the same used in Method 1 e. The model for currency is trained with pairs features target between times and. The prediction set includes only one pair: the features computed between and and the target computed at of currency.

Method 3. The third method is based on long short-term memory networks, a special kind of recurrent neural networks, capable of learning long-term dependencies. As for Method 2, we build a different model for each currency. Each model predicts the ROI of a given currency at day based on the values of the ROI of the same currency between days and included.

Baseline Method. As baseline method, we adopt the simple moving average strategy SMA widely tested and used as a null model in stock market prediction [ 57 — 60 ]. It estimates the price of a currency at day as the average price of the same currency between and included. We compare the performance of various investment portfolios built based on the algorithms predictions.

The investment portfolio is built at time by equally splitting an initial capital among the top currencies predicted with positive return. Hence, the total return at time is The portfolios performance is evaluated by computing the Sharpe ratio and the geometric mean return.

The Sharpe ratio is defined as where is the average return on investment obtained between times 0 and and is the corresponding standard deviation. The geometric mean return is defined as where corresponds to the total number of days considered.

The cumulative return obtained at after investing and selling on the following day for the whole period is defined as. The number of currencies to include in a portfolio is chosen at by optimising either the geometric mean geometric mean optimisation or the Sharpe ratio Sharpe ratio optimisation over the possible choices of.

The same approach is used to choose the parameters of Method 1 and , Method 2 and , and the baseline method. We predict the price of the currencies at day , for all included between Jan 1, , and Apr 24, To discount for the effect of the overall market movement i. This implies that Bitcoin is excluded from our analysis. First, we choose the parameters for each method. Parameters include the number of currencies to include the portfolio as well as the parameters specific to each method.

In most cases, at each day we choose the parameters that maximise either the geometric mean geometric mean optimisation or the Sharpe ratio Sharpe ratio optimisation computed between times 0 and. Baseline Strategy. We test the performance of the baseline strategy for choices of window the minimal requirement for the to be different from 0 and.

We find that the value of mazimising the geometric mean return see Appendix Section A and the Sharpe ratio see Appendix Section A fluctuates especially before November and has median value 4 in both cases.

The number of currencies included in the portfolio oscillates between 1 and 11 with median at 3, both for the Sharpe ratio see Appendix Section A and the geometric mean return see Appendix Section A optimisation.

We explore values of the window in days and the training period in days see Appendix Section A. We find that the median value of the selected window across time is 7 for both the Sharpe ratio and the geometric mean optimisation. The median value of is 5 under geometric mean optimisation and 10 under Sharpe ratio optimisation.

The number of currencies included in the portfolio oscillates between 1 and 43 with median at 15 for the Sharpe ratio see Appendix Section A and 9 for the geometric mean return see Appendix Section A optimisation. We explore values of the window in days and the training period in days see Appendix, Figure The median value of the selected window across time is 3 for both the Sharpe ratio and the geometric mean optimisation.

The median value of is 10 under geometric mean and Sharpe ratio optimisation. The number of currencies included has median at 17 for the Sharpe ratio and 7 for the geometric mean optimisation see Appendix Section A. The LSTM has three parameters: The number of epochs, or complete passes through the dataset during the training phase; the number of neurons in the neural network, and the length of the window. These parameters are chosen by optimising the price prediction of three currencies Bitcoin, Ripple, and Ethereum that have on average the largest market share across time excluding Bitcoin Cash that is a fork of Bitcoin.

Results see Appendix Section A reveal that, in the range of parameters explored, the best results are achieved for. Results are not particularly affected by the choice of the number of neurones nor the number of epochs.

We choose 1 neuron and epochs since the larger these two parameters, the larger the computational time. The number of currencies to include in the portfolio is optimised over time by mazimising the geometric mean return see Appendix Section A and the Sharpe ratio see Appendix Section A. In both cases the median number of currencies included is 1.

## The Use of Control Charts in the Study of Bitcoin’s Price Variability

Bitcoin achieved a remarkable rise in in spite of many things that would normally make investors wary, including US-China tensions, Brexit and, of course, an international pandemic. So what has driven this huge price appreciation and is it different to the bubble of ? One reason for the massive price rise is that there has been a big influx of investors from large-scale institutions such as pension schemes, university endowment funds and investment trusts. This time, big names such as billionaire investor Paul Tudor Jones and insurance giant MassMutual have invested heavily, while even former naysayers like JP Morgan now say that bitcoin could have a bright future.

## Inflation Hit a 40-Year High in December. Here’s What Costs More, and How to Plan for It

Subscriber Account active since. Yield farming is a means of earning interest on your cryptocurrency, similar to how you'd earn interest on any money in your savings account. And similarly to depositing money in a bank, yield farming involves locking up your cryptocurrency, called " staking ," for a period of time in exchange for interest or other rewards, such as more cryptocurrency. Since yield farming began in , yield farmers have earned returns in the form of annual percentage yields APY that can reach triple digits. But this potential return comes at high risk, with the protocols and coins earned subject to extreme volatility and rug pulls wherein developers abandon a project and make off with investors' funds. Also known as liquidity farming, yield farming works by first allowing an investor to stake their coins by depositing them into a lending protocol through a decentralized app, or dApp. Other investors can then borrow the coins through the dApp to use for speculation , where they try to profit off of sharp swings they anticipate in the coin's market price. Blockchain-based apps offer incentives for users to provide liquidity by locking up their coins in a process called staking. Investors who lock up their coins on the yield-farming protocol can earn interest and often more cryptocurrency coins — the real boon to the deal. If the price of those additional coins appreciates, the investor's returns rise as well.

## What Determines the Price of Bitcoin?

More cryptocurrency trading goes on in Nigeria than almost anywhere else in the world, reflecting a loss of faith in more traditional forms of investment, as Ijeoma Ndukwe reports. Tola Fadugbagbe recalls moving to Lagos from his small south-western town 10 years ago with dreams of brighter prospects. Instead, the year-old ended up in a series of odd jobs earning the minimum wage to survive - a typical story for many young Nigerians who are just trying to get by. It was not until that online adverts for Bitcoin piqued his interest and he began his cryptocurrency journey.

## RAAX Evolves with Bitcoin

Energy consumption has become the latest flashpoint for cryptocurrency. Critics decry it as an energy hog while proponents hail it for being less intensive than the current global economy. This puts the bitcoin economy on par with the carbon dioxide emissions of a small, developing nation like Sri Lanka or Jordan. Jordan, in particular, is home to 10 million people. But CoinMetrics data indicates more than 1 million bitcoin addresses are active, daily, out of up to million accounts active in the past decade, as tallied by the exchange Crypto. Plus, many bitcoin mining businesses rely on environmentally friendly energy sources like hydropower and capturing natural gas leaks from oil fields.

## The rise of using cryptocurrency in business

Please change the wallet network. Change the wallet network in the MetaMask Application to add this contract. Harvest Finance. United States Dollar. Harvest Finance is down 6. It has a circulating supply of , FARM coins and a max. If you would like to know where to buy Harvest Finance, the top cryptocurrency exchanges for trading in Harvest Finance stock are currently Binance , Mandala Exchange , Gate. You can find others listed on our crypto exchanges page.

Agricultural commodity ETFs are funds that invest in companies that produce agriculture products such as grains, dairy and livestock. These funds can invest in a bundle of commodity types, or focus on one specific commodity. Assets and Average Volume as of

The blockchain revolution is coming. Start-ups, Fortune companies and governments alike are all experimenting with the promising technology. This is especially true in the agricultural sector where the technology could be used to improve food traceability, supply chain management and payment options. At its core, blockchain is an electronic system that allows for record-keeping of transactions in real time. When participants in a blockchain system complete a transaction, the time, date, nature and cost of the exchange is recorded.

These objective indicators identify the segments with positive expected returns. Then, using correlation and volatility, an optimization process determines the weight to these segments with the goal of creating a portfolio with maximum diversification while reducing risk. The expanded PDF version of this commentary can be downloaded here. January started off strongly, RAAX was up 5. RAAX invests in three types of real assets: financial assets, income assets and resource assets.

Try out PMC Labs and tell us what you think. Learn More. In this study, we explored the impact of COVID on the cross-correlations between crude oil and agricultural futures markets.

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