ISSN 2379-5980 (online) DOI 10.5195/LEDGER.2024.325


Reconciling Open Interest with Traded Volume in Perpetual Swaps

Ioannis Giagkiozis,∗† Emilio Said

Abstract. Perpetual swaps are derivative contracts that allow traders to speculate on, or hedge, the price movements of cryptocurrencies. Unlike futures contracts, perpetual swaps have no settlement or expiration in the traditional sense. The funding rate acts as the mechanism that tethers the perpetual swap to its underlying with the help of arbitrageurs. Open interest, in the context of perpetual swaps and derivative contracts in general, refers to the total number of outstanding contracts at a given point in time. It is a critical metric in derivatives markets as it can provide insight into market activity, sentiment and overall liquidity. It also provides a way to estimate a lower bound on the collateral required for every cryptocurrency market on an exchange. This number, cumulated across all markets on the exchange in combination with proof of reserves, can be used to gauge whether the exchange in question operates with unsustainable levels of leverage, which could have solvency implications. We find that open interest in Bitcoin perpetual swaps is systematically misquoted by some of the largest derivatives exchanges; however, the degree varies, with some exchanges reporting open interest that is wholly implausible and others that seem to be delaying messages of forced trades, i.e., liquidations. We identify these incongruities by analyzing tick-by-tick data for two time periods in 2023 by connecting directly to seven of the most liquid cryptocurrency derivatives exchanges.



† I. Giagkozis ( is Director at Chrysor Trading, Abu Dhabi, UAE.

‡ E. Said ( is Quantitative Researcher at Abu Dhabi Investment Authority, Strategy and Planning Department, Quantitative Research and Development, Abu Dhabi, UAE.


  1. Introduction

    Perpetual swaps were introduced by BitMEX in 2016.1 They are futures contracts with no expiry. These contracts allow for high leverage with most cryptocurrency exchanges offering leverage in the range of 100x–125x and some recent platforms allowing up to 1000x(!) leverage.2 Interestingly a reduction in allowed leverage, in retrospect, can be a sign that an exchange is in distress.3 These contracts are designed to track an underlying exchange rate, e.g., BTC/USD, such that speculators can gain exposure to that underlying while holding a collateral of their choice (usually USDT). The first perpetual swap was what is commonly referred to today as inverse perpetual. In inverse perpetual contracts, profits and losses as well as margin are paid in the base asset, e.g., BTC for the BTC/USD inverse perpetual, while the price of the contract is quoted in units of the quote asset. Except for their use as an instrument for speculation, this kind of perpetual swap was originally also used as a tool to hedge exposure to the underlying. This can be achieved by opening a short position in the contract with 1x leverage. As the inverse perpetual is coin margined, every change in the price of the underlying is offset by the short position in the inverse perpetual which results in a stable equity curve when denominated in units of the quote asset. Before USDT was accepted as the de facto stablecoin in cryptocurrency trading, this was the primary mechanism for traders to hedge their Bitcoin exposure.

    As stablecoins gained in popularity, inverse perpetuals ceded market dominance to the linear perpetual swap. These pay profits and losses in the quote asset and are margined by the same, which is most often some stablecoin for the US dollar. This is not unexpected, as for most people their numéraire is some form of FIAT currency, predominantly USD, especially when it comes to financial instruments. However, the benefits of the linear perpetual are mostly limited to their use as an instrument for speculation, given that these contracts are margined by the quote asset. This margining implies the existence of a liquidation price which necessitates active monitoring and rebalancing if used as a hedge. In addition, most exchanges have a limit on the size of the position that can be opened on linear perpetuals, which reduces the capacity of a potential hedge. Originally, the mechanism used to tether perpetual swaps to the underlying was the interest rate differential for USD and Bitcoin on the Bitfinex lending market.1 Namely, if it was relatively more expensive to borrow USD than Bitcoin, then long positions paid short positions the difference, in lending rates multiplied by the individual position size, and shorts paid long positions if the borrow rate of Bitcoin was higher than USD. These cashflows were automatically exchanged every 8 hours among market participants who held positions at the 8-hour mark, with no fees charged by the exchange. At the time, cryptocurrency markets were not as strongly coupled as they are presently, which led to frequent dislocations between the price of the contract and the underlying. Today, most exchanges use some variation of what is referred to as the funding rate. The funding rate is computed as a function of the perpetual swap price Ft, and an index price which is a weighted average of the SPOT market of the underlying, St, over a number of exchanges. The details vary from exchange to exchange; however, this function is constructed in such a way as to incentivize short positions by means of funding payments when Ft > St and long positions when Ft < St. Price dislocations with this mechanism can and do happen, but they are much less frequent and smaller in magnitude.4

    Centralized cryptocurrency exchanges offer their market data feeds freely. The majority give access to level 2 data even to anonymous clients: with the appropriate software anyone can collect the data reported by these exchanges in real-time. Some exchanges even offer historical archival services of tick level data (see the Appendix). This is in stark contrast to traditional exchanges, e.g., the NASDAQ and NYSE, whose historical and real-time tick-by-tick market data and feeds are behind exorbitant paywalls. From that perspective, cryptocurrency exchanges have introduced a much higher standard of transparency and inclusivity—something that we believe ought to be acknowledged, especially in an environment where narratives tend to be highly polarized and one-sided. That said, the record of cryptocurrency exchanges is far from spotless. There have been numerous reports alleging fake volume,5, 6 wash trading,7, 8 market manipulation,9–11 pump-and-dump schemes,12 etc., although we should not lose sight of the fact that these ill-conceived practices, including the terminology, have their origins in traditional markets.13–15 That is to say, in our view, open ledgers, data, connectivity, and trading accessible to everyone is a model that is much more likely to weed out bad actors more quickly and effectively, leading to free and open markets with efficient price discovery. The alternative would be the current mode of operation, where access to markets is restricted and obscured behind several intermediaries, resulting in rent-seeking behavior with price discovery taking a back seat.16

  2. Open Interest and Perpetual Contracts

    Open interest is the total number of outstanding contracts in a market. Similarly to trading volume it is an indicator of activity in that market.17 By definition, open interest can only change when the number of outstanding contracts changes. This can only happen in the following situations:

  3. Data

    Table 1. List of markets considered in this work.

    Exchange Symbol Contract Kind Sumbol URL
    ByBit BTC_USD_IP Inverse Perpetual
    ByBit BTC_USDT_P Linear Perpetual
    Deribit BTC_USD_IP Inverse Perpetual
    Binance BTC_USD_IP Inverse Perpetual
    Binance BTC_USDT_P Linear Perpetual
    BitMEX BTC_USD_IP Inverse Perpetual
    OKX BTC_USD_IP Inverse Perpetual
    OKX BTC_USDT_P Linear Perpetual
    Kraken BTC_USD_IP Inverse Perpetual
    Kraken BTC_USD_P Linear Perpetual
    HTX BTC_USD_IP Inverse Perpetual
    HTX BTC_USDT_P Linear Perpetual

    Our datasets are comprised of tick-by-tick trades, block trades, liquidations, and open interest as reported by the APIs of the respective exchanges mentioned in Table 1. We limit our attention to Bitcoin linear perpetuals quoted in USDT ( and inverse perpetuals quoted in USD, as these are the most liquid derivatives. We focus on two periods: i) 2023/01/01 to 2023/01/31 (Period 1) which is the beginning of the year and is usually a period of naturally higher trading volume and ii) 2023/07/01 to 2023/09/30 (Period 2) containing most of the summer of months of 2023 and September as it is the most recent month prior to this work which enables us to see if our observations are still pertinent in recent data. The infrastructure as well as the collected data are proprietary; however, in the interest of encouraging reproduction of this work, we offer a few suggestions on free and open source resources that can help in that respect (please see the Appendix for further details).

    The exchanges in Table 1 have been selected on the basis of volume (as seen, for one example, at CoinMarketCap),21 the fact that they have been in operation relatively long enough, and that the trading community considers them reasonably legitimate venues for trading—notwithstanding any allegations by regulators that have yet to be proven.22

    The first set of results can be seen in Table 2 and Table 3 for Periods 1 and 2 respectively. In these tables we show the open interest total variation as defined by Equation (3), the total volume (Equation (4)) and the excess total variation (Equation (6)) for the entire periods, namely these are cumulative results over the periods of January 2023 and July to September (inclusive) 2023 (Periods 1 and 2, respectively). As can be seen for Period 1, only ByBit exhibits excess total variation on this scale while the rest of the exchanges seem to be reporting open interest changes that can be explained by the reported trading volume. In Period 2 however, the results are significantly different (see Table 3). In this period, both ByBit and OKX have non-negligible excess total variation. The excess in the BTC_USDT_P and BTC_USD_IP markets on ByBit has grown quite dramatically, both in absolute terms and in proportion to the reported volume. Furthermore, in this period BTC_USD_IP on Binance starts showing signs of misreporting. The rest of the exchanges do not seem to have excess in this period either, at least when considering the period in its entirety.

    To put the observed excess on ByBit into perspective, if the open interest reported by it in Period 2 is to believed for the BTC_USDT_P market, the lower bound on the trading volume required to produce the observed changes in open interest would be more than $128bn. However, this minimum requires that all trading volume in every reporting interval is in the direction that increases (or decreases) open interest. With so much volume, and presumably participants, it would be unrealistic to assume that level of trading synchronicity. If we apply ratios of \(O_{TV}/V_T\) from exchanges that do not misreport open interest (at least not to that degree) and apply them to the \(O_{TV}\) for the BTC_USDT_P market on ByBit, that would imply trading volumes in the range of $156bn to $213bn, i.e., greater than the volume on the BTC_USDT_P market on Binance(!); which in our view is highly improbable.

    Considering that the periods are quite lengthy in comparison with the open interest reporting period in all the exchanges under consideration, we elected to refine the resolution in the hope of gleaning more insight in the observed excess total variation in Tables 2 and 3. With that objective in mind we chose three more sub-periods: i) one day (1D), ii) one hour (1H), and iii) one minute (1min), within Periods 1 and 2. For each one of those sub-periods we computed: i) the probability of observing excess total variation for the sub-period SPwithin each period \(\mathbb{P}_{SP}(X_{TV} > 0)\), and ii) the expectation of the excess total variation conditional on this excess being greater than 0: \(\mathbb{E}_{SP}[X_{TV} | X_{TV} > 0]\). These results can be seen in Table 4 and Table 5 for Periods 1 and 2 respectively. At these finer resolutions we see that virtually all the exchanges have less than ideal open interest reporting practices. For instance, we can see on ByBit that the reported open interest cannot be reconciled with the trading volume on any of the selected sub-periods. In essence, the open interest is incorrect every day, almost every hour, and in more than 70% of the one minute sub-periods. It is particularly impressive that the expected excess total variation in the one minute sub-period for the BTC_USDT_P market on ByBit has a size that is more than $500,000. In fact, it is quite surprising that only HTX and Kraken (and perhaps to some degree BitMEX) report open interest that can almost be reconciled on every sub-period.



    Table 2. Open interest total variation, total trading volume and excess total variation for the period 2023/01/01 to 2023/01/31 (inclusive). For comparison purposes we have converted Bitcoin (B) units to USD using the average price of the exchange rate for the period which was $20,625 per bitcoin.

    Exchange Symbol $$O_{TV}$$ $$V_T$$ $$X_{TV}$$
    ByBit BTC_USDT_P ₿2,213,583 ($45.66B) ₿1,469,962 ($30.32B) ₿743,622 ($15.34B)
    ByBit BTC_USD_IP $12,088,654,910 $6,570,819,230 $5,517,835,680
    Binance BTC_USDT_P ₿2,084,275 ($42.99B) ₿4,050,268 ($83.54B) ₿0 ($0)
    OKX BTC_USDT_P ₿905,525 ($18.68B) ₿949,431 ($19.58B) ₿0 ($0)
    BitMEX BTC_USD_IP $4,065,203,600 $5,599,999,100 $0
    OKX BTC_USD_IP $3,924,367,800 $5,325,455,400 $0
    Deribit BTC_USD_IP $3,665,856,750 $4,045,272,670 $0
    HTX BTC_USDT_P ₿133,615 ($2.76B) ₿381,464 ($7.87B) ₿0 ($0)
    Kraken BTC_USD_P ₿25,895 ($534.08M) ₿35,024 ($722.37M) ₿0 ($0)
    HTX BTC_USD_IP $234,509,100 $632,036,900 $0
    Kraken BTC_USD_IP $201,790,503 $315,671,226 $0
    Binance BTC_USD_IP $86,462,494 $120,899,920 $0

    Table 3. Open interest total variation, total trading volume, and excess total variation for the period 2023/07/01 to 2023/09/30 (inclusive). Similarly to the previous table we use the average price for the period to facilitate comparisons. The average price for the period was $28,250 per bitcoin.

    Exchange Symbol $$O_{TV}$$ $$V_T$$ $$X_{TV}$$
    ByBit BTC_USDT_P ₿4,583,448 ($129.48B) ₿2,571,288 ($72.64B) ₿2,012,160 ($56.84B)
    OKX BTC_USDT_P ₿3,213,509 ($90.78B) ₿2,598,848 ($73.42B) ₿614,661 ($17.36B)
    ByBit BTC_USD_IP $22,856,881,902 $10,417,205,910 $12,439,675,992
    OKX BTC_USD_IP $11,568,080,200 $10,203,157,300 $1,364,922,900
    Binance BTC_USD_IP $323,617,912 $285,387,614 $38,230,298
    Binance BTC_USDT_P ₿5,899,253 ($166.65B) ₿7,106,811 ($200.77B) ₿0 ($0)
    BitMEX BTC_USD_IP $12,067,917,700 $16,241,861,300 $0
    Deribit BTC_USD_IP $10,316,342,380 $10,419,942,140 $0
    HTX BTC_USDT_P ₿266,235 ($7.52B) ₿741,751 ($20.95B) ₿0 ($0)
    Kraken BTC_USD_P ₿77,984 ($2.2B) ₿107,353 ($3.03B) ₿0 ($0)
    HTX BTC_USD_IP $456,969,000 $1,289,953,500 $0
    Kraken BTC_USD_IP $446,044,392 $733,864,191 $0

    We can also see that the expected excess total variation seems to be increasing in Period 2 (P2) compared with Period 1 (P1) on some exchanges. For example, on OKX for the BTC_USDT_P market \(\mathbb{E}_{1D}[X_{TV} | X_{TV} > 0]\) is ∼ 4 times higher for the 1D sub-period and ∼ 2 times higher when considering the 1H sub-period. A similar pattern can be seen on ByBit, Binance, and to some extent on Deribit and HTX, although on Deribit it is considerably less pronounced. Namely, incongruities are amplified during the summer months on some exchanges, at least in the two periods considered in this work.

    Table 4. Probability of excess total variation and expected excess total variation for the period 2023/01/01 to 2023/01/31 (inclusive). Average price $20,625 per bitcoin.

    Exchange Symbol $$\mathbb{P}_{1D}(X_{TV} > 0)$$ $$\mathbb{E}_{1D}[X_{TV} | X_{TV} > 0]$$ $$\mathbb{P}_{1H}(X_{TV} > 0)$$ $$\mathbb{E}_{1H}[X_{TV} | X_{TV} > 0]$$ $$\mathbb{P}_{1min}(X_{TV} > 0)$$ $$\mathbb{E}_{1min}[X_{TV} | X_{TV} > 0]$$
    ByBit BTC_USD_IP 100.0% $177,994,699 98.9% $7,633,055 70.8% $200,682
    ByBit BTC_USDT_P 100.0% ₿23,988 ($494.75M) 98.5% ₿1,043 ($21.51M) 72.4% ₿28 ($577.14K)
    OKX BTC_USDT_P 51.6% ₿1,992 ($41.08M) 70.8% ₿159 ($3.29M) 51.2% ₿9 ($181.63K)
    OKX BTC_USD_IP 12.9% $6,585,500 31.3% $805,784 29.9% $59,521
    Deribit BTC_USD_IP 12.9% $4,856,062 29.8% $719,027 28.9% $46,847
    Binance BTC_USD_IP 6.5% $205,770 45.4% $15,317 39.9% $1,102
    Kraken BTC_USD_P 6.5% ₿93 ($1.92M) 22.6% ₿8 ($163.46K) 14.7% ₿1 ($16.56K)
    Kraken BTC_USD_IP 3.2% $166,194 12.0% $39,292 8.7% $5,996
    BitMEX BTC_USD_IP 0.0% $0 17.2% $702,462 28.3% $57,394
    HTX BTC_USD_IP 0.0% $0 1.5% $54,136 6.2% $9,267
    Binance BTC_USDT_P 0.0% ₿0 ($0) 0.4% ₿259 ($5.34M) 14.0% ₿19 ($398.15K)
    HTX BTC_USDT_P 0.0% ₿0 ($0) 0.0% ₿0 ($0) 11.5% ₿2 ($35.8K)

    Table 5. Probability of excess total variation and expected excess total variation for the period 2023/07/01 to 2023/09/30 (inclusive). Average price $28,250 per bitcoin.

    Exchange Symbol $$\mathbb{P}_{1D}(X_{TV} > 0)$$ $$\mathbb{E}_{1D}[X_{TV} | X_{TV} > 0]$$ $$\mathbb{P}_{1H}(X_{TV} > 0)$$ $$\mathbb{E}_{1H}[X_{TV} | X_{TV} > 0]$$ $$\mathbb{P}_{1min}(X_{TV} > 0)$$ $$\mathbb{E}_{1min}[X_{TV} | X_{TV} > 0]$$
    ByBit BTC_USDT_P 100.0% ₿21,871 ($617.86M) 99.8% ₿918 ($25.92M) 75.8% ₿22 ($627.06K)
    ByBit BTC_USD_IP 100.0% $135,213,869 99.6% $5,658,882 70.1% $146,323
    OKX BTC_USDT_P 96.7% ₿7,070 ($199.73M) 92.7% ₿343 ($9.69M) 59.7% ₿13 ($361.45K)
    Binance BTC_USD_IP 88.0% $579,256 83.2% $37,878 48.7% $2,232
    OKX BTC_USD_IP 85.9% $21,183,446 72.5% $1,395,272 42.2% $72,655
    Deribit BTC_USD_IP 52.2% $6,708,834 44.0% $893,163 29.1% $59,624
    Binance BTC_USDT_P 27.2% ₿4,893 ($138.22M) 41.1% ₿445 ($12.56M) 33.4% ₿39 ($1.09M)
    Kraken BTC_USD_P 4.3% ₿80 ($2.26M) 20.6% ₿9 ($268.19K) 15.6% ₿1 ($20.91K)
    BitMEX BTC_USD_IP 3.3% $5,323,233 28.5% $579,009 32.0% $54,281
    Kraken BTC_USD_IP 0.0% $0 6.4% $34,787 5.9% $6,331
    HTX BTC_USD_IP 0.0% $0 1.0% $62,557 4.3% $6,371
    HTX BTC_USDT_P 0.0% ₿0 ($0) 0.4% ₿19 ($526.5K) 11.3% ₿2 ($43.75K)

    One analysis that we considered performing was to normalize the expectations in Tables 4 and 5 by the mean trading volume typical for that sub-period; however, given that we cannot be sure if it is the open interest that is incorrect or the reported trading volume (or both), it is preferable that all quantities remain in absolute and not relative terms.

  4. Discussion

    First let us summarize our findings from Section 4.

  5. Conclusions

In this work we consider the most liquid Bitcoin perpetual swaps on seven of the top cryptocurrency exchanges. We find that trading volume cannot be reconciled with the reported changes in open interest for the majority of these exchanges. It is unclear whether this is due to delayed or unreported trading volume or due to incorrectly reported open interest. In our view, the most likely scenario is that both are true, perhaps, however, not to the same degree on every exchange. Although we could not perfectly reconcile these quantities for any of the exchanges in question, we find that there are discernible differences in behavior across these exchanges. The discrepancies on ByBit and OKX are so frequent and large in magnitude that these two exchanges merit a category of their own. On these exchanges we could not reconcile trading volume with reported open interest in any time period, with the implied trading volume being in the range of hundreds of billions over and above the reported trading volume, assuming the open interest is the quantity that is correct. If in fact, however, the trading volume is the more accurately reported quantity, this would imply that the open interest on these exchanges is almost completely fabricated. This could perhaps be explained by certain incentive structures baked into the scenario: leading market participants to believe that informed investors are taking large positions in these markets (as implied by the large change in open interest) could—depending on the participants’ prior positioning—lead to panic or fear of missing out on potential profits, thereby increasing trading volume, and profit for the exchange. Given that volatility and trading volumes in Bitcoin and other cryptocurrencies have been trending lower in 2023 we believe that the latter is a more plausible explanation. Figures 1 and 2 also seem to point in that direction.

Binance, Deribit and BitMEX form, conceptually, another cluster of exchanges. Although we could not reconcile the changes in open interest with trading volume, the frequency and magnitude of the discrepancies is such that it leaves room for some relatively more benign explanation (see Section 5). The last group of exchanges is formed by Kraken and HTX, who have the lowest number of discrepancies. For these exchanges we could reconcile changes in open interest with trading volume on almost all sub-periods (see Tables 4 and 5).

What applies to all exchanges, however, is that even if there exists some explanation, the fact remains that market data is systematically misreported to varying degrees—be it in the form of a delay, omission, or fabrication. Such inaccuracies could easily be exploited by these exchanges to manipulate the market participants’ perception of price evolution, even if it is for short periods of time. If there is a lesson to be learned by the relatively recent demise of Alameda and FTX, it is that if something can be exploited, it most likely will be exploited.23 The solution to this would be, from a technical perspective, very easy to implement: simply add open interest to every trade pushed out by the market feeds, and report all trades without delay—irrespective of their kind—to all market participants with the correct price, size, and timestamp of when the trade took place, ideally with microsecond precision. As open interest is, in a way, a representation of the total outstanding liabilities in a market, it should be treated with the same rigor as proof of reserves.

Lastly, we would like to call upon the exchanges examined in this work to evaluate carefully the evidence presented herein, in the hope that they will take our suggestions into consideration and demonstrate with their actions their commitment to their users and free and open markets. If this industry is to grow and flourish, we would be well served to remember the principles and ethos of the people that put in place its foundation.24

Author Contributions

The platform and data used for this work are the property of Ioannis Giagkiozis. With the aim of minimising potential errors both authors arrived at the results presented in this work first independently and then once more jointly. All authors contributed equally to the analysis and discussion of the findings.

Conflict of Interest

The authors declare that they have no known conflicts of interest as per the journal’s Conflict of Interest Policy.

Notes and References

1 Hayes, A. “Announcing the Launch of the Perpetual XBTUSD Leveraged Swap.” BitMEX (accessed 4 March 2024)

2 Reference intentionally omitted as trading with such high leverage, especially in volatile markets like cryptocurrencies, is ill-advised. 1000x leverage implies, in the best case, that the liquidation price is 10 basis points away from the entry price and more realistically 5 basis points considering fees.

3 Reynolds, K. “FTX Cuts Leverage Limit to 20x From 100x as Criticism of Margin Trading in Crypto Grows.” CoinDesk (accessed 4 March 2024)

4 He, S., Manela, A., Ross, O., von Wachter, V. “Fundamentals of Perpetual Futures.” SSRN (accessed 4 March 2024)

5 No Author. “Investigation into the Legitimacy of Reported Cryptocurrency Exchange Volume.” Alameda Research (accessed 4 March 2024) copy available via the Wayback Machine at

6 Chen, J., Lin, D., Wu, J. “Do Cryptocurrency Exchanges Fake Trading Volumes? An Empirical Analysis of Wash Trading Based on Data Mining.” Physica A: Statistical Mechanics and its Applications 586 126405 (January 2022)

7 Cong, L., Li, X., Tang, K., Yang, Y. “Crypto Wash Trading.” SSRN (accessed 4 March 2024)

8 Le Pennec, G., Fiedler, I., Ante, L. “Wash Trading at Cryptocurrency Exchanges.” Finance Research Letters 43 101982 (November 2021)

9 Gandal, N., Hamrick, J., Moore, T., Oberman, T. “Price Manipulation in the Bitcoin Ecosystem.” Journal of Monetary Economics 95 86–96 (May 2018)

10 Chen, W., Wu, J., Zheng, Z., Chen, C., Zhou, Y. “Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network.” arXiv (19 January 2019) (accessed 4 March 2024)

11 Peterson, T. “To the Moon: A History of Bitcoin Price Manipulation.” SSRN (14 September 2020) (accessed 4 March 2024)

12 Li, T., Shin, D., Wang, B. “Cryptocurrency Pump-and-Dump Schemes.” SSRN (23 October 2018) (accessed 4 March 2024)

13 Jarrow, R. A. “Market Manipulation, Bubbles, Corners, and Short Squeezes.” The Journal of Financial and Quantitative Analysis 27.3 311–336 (September 1992)

14 Bradshaw, M. T., Richardson, S. A., Sloan, R. G. “Pump and Dump: An Empirical Analysis of the Relation Between Corporate Financing Activities and Sell-Side Analyst Research.” SSRN (22 May 2003) (accessed 4 March 2024)

15 Kim, K. A., Park, J. “Why Do Price Limits Exist in Stock Markets? A Manipulation-Based Explana- tion.” European Financial Management 16.2 296–318 (March 2010)

16 Khwaja, A. I., Mian, A. “Rent Seeking and Corruption in Financial Markets.” Annual Review of Economics 3.1 579–600 (September 2011)

17 The definition of volume used in this study is the standard definition of volume reported by exchanges for every trade. We refer the reader not familiar with this notion to the following definition:

18 A liquidation is a forced trade by the exchange which takes place when one of the counter-parties has reached critical margin levels and can no longer maintain the position open. Liquidations are an automated risk reduction mechanism.

19 The matching and risk engines are crucial components of an exchange platform. The first one ensures that trades are executed efficiently and fairly following the principles of price-time priority. The second one ensures the safety and security of all funds and counter-party solvency by triggering liquidations when needed and ensuring that new positions do not exceed available margin.

20 No Author. “General API Information.” Binance (accessed 22 March 2024)

21 No Author. “Cryptocurrency Prices, Charts And Market Capitalizations.” CoinMarketCap (accessed 4 March 2024)

22 No Author. “SEC Files 13 Charges Against Binance Entities and Founder Changpeng Zhao.” U.S. Securities and Exchange Commission (accessed 4 March 2024)

23 No Author. “Decl. John J. Ray III in re: FTX Trading Ltd. et. al.United States Bankruptcy Court for the Disctrict of Delaware Case No. 22-11068, Doc. 24 (D. Del. 30 May 2023) (accessed 4 March 2024) Available at

24 Nakamoto, S. “Bitcoin: A Peer-to-Peer Electronic Cash System.” (2008) (accessed 4 March 2024)

Appendix: Open Source API Connectors and Data Sources

There are many libraries and tick-by-tick data providers for the cryptocurrency markets. We cannot endorse any commercial products. We restrict our focus on free and open source libraries as well as data sources.

In terms of libraries we would recommend ccxt ( which has connectivity to more than 90 cryptocurrency exchanges, including the exchanges that we focus on this work. This library should prove sufficient for collecting the data on which this work is based on. If a custom solution is required, as we have already mentioned, each of these exchanges has an API that users can collect data even without account. We refer our readers to the respective exchange websites that are available in Table 1 for the REST and websocket API documentation. Binance, which is the leader in this space has gone the extra mile and offers level 2 data directly on their website. Order book data has to be requested specifically, but as of the of this writing Binance does not apply any charge for such data. These are available at Binance Historical Market Data (