CRYPTO TRADE ANALYSIS
Our client is a highly regarded global online currency exchange, which provides the public with a platform to easily convert between fiat and cryptocurrencies in Australia and internationally.
The business had undergone a period of rapid growth and was reviewing its trading history and risk management systems. Parity were asked to undertake a quantitative analysis project to provide insight into customers’ behaviour, per-transaction profitability, and the associated risks. The project was deemed to be business critical, and the review was required within a tight timeframe.
The key deliverables were:
- Provide a detailed profit and loss analysis for all historical cryptocurrency transactions
- Identify and quantify the key areas of trading risk seen in the transaction data
- Provide guidance for the development of trading and risk management algorithm
Parity worked closely with the trading and finance teams to understand and map the trading processes, as well as identify the key data sources. The data sets were cleansed, analysed in Python and Excel to evaluate the P&L impact of each trade, and segmented to determine how customers’ behaviour impacted profitability. An operations framework was developed to provide guidance on what crypto balances to hold, when to transact, and when to use derivatives. The results and recommendations were documented and presented to senior management.
The analysis provided management with a view into the profitability according to customer segment, across time and under various market conditions. It also provided insight into the key trading risks the business was exposed to and set out a roadmap for addressing the key operating exposures including the likelihood of trades completing, and the potential profit or loss from incomplete trades. Working under tight time constraints, Parity delivered the analysis and recommendations within 7 business days.
“Working under tight time constraints, Parity delivered the analysis and recommendations within 7 business days.”
Brownlow Medal Prediction Using Excel Based Machine LearningShare this articlelinkedintwitterA spreadsheet-based regression model using Neural Networks to rank this year’s medal contendersBy Stephen Huppert and Jack LanghammerIt may not be September, and the finals...
Our client is a leading Australian independent school that regularly places in the top 10 schools for academic results in Victoria. The client needed various…
The trustee to one of the leading industry super funds in Australia was evaluating alternative strategies to reduce costs to its members, including the option…