Revealing the Crypto macro analysis methodology of top researchers
Guest:
- Zheng @ZnQ_626
• LUCIDA Founder
• 2019 Bgain Digital Asset Trading League Season 1 Mixed Strategy Group Champion
• 2020 TokenInsight Global Asset Quantification Competition, runner-up in April, champion in May, and third runner-up in the season in the composite strategy group
• 2021 TokenInsight x KuCoin Global Asset Quantification Competition, third place in the season in the composite strategy group - Vivienna @VV_watch
• BuilderRocket Accelerator Research Partner
• Entered the industry in 2017
• ex Foxconn's blockchain investment fund does investment research
• ex Huobi Defi researcher
• Obsessed with macro research - HighFreedom @highfree2028
• Entered the industry in 2016
• Comprehensive background in computer & finance
• Currently a researcher at a securities firm
• Good at dollar-based Liquidity analysis and macro analysis for cycle timing - Albert @assassinaden
• Quantitative private equity fund manager
• Former quantitative researcher in the foreign exchange market, doing statistical arbitrage and relative value strategies
• Good at non-delta strategies and macro research
• Pay attention to response Cycles and the ability to travel through bulls and bears
Revealing the macro analysis framework
Zheng@LUCIDA:
With the development of the Crypto industry, the correlation between the market and the macro economy is getting higher and higher, and macro has become more and more important for analyzing the market A necessary part. Today I would like to specifically talk to you about macro-related topics. Let’s start with the first question, which is to ask you to share your own framework and methodology for analyzing macroeconomics, as well as the underlying logic.
HighFreedom:
Crypto-oriented macro analysis consists of two parts: the first type is off-site (non-crypto native) macro, and the second type is on-site macro (crypto native), the core is BTC on-chain data analysis )
For the first type of off-site macro, my analysis framework is a bit like an inverted triangle, divided into three levels.
The first layer is various data, such as employment, GDP, inflation, PCE, etc.
The second level is to summarize the data. The data in the first layer may seem messy, but in fact we can finally divide all kinds of data into two categories: economic good and bad data and inflation high and low data. Because the ultimate goal of the Federal Reserve, which formulates monetary policy (cutting interest rates, shrinking and expanding the balance sheet, etc.) and the Treasury, which formulates fiscal policy (how the government spends money, etc.), is to maximize employment and price stability. In other words, the core goal of these two institutions is to ensure that the economy is good enough and inflation is under control.
The third layer is the specific composition of US dollar liquidity and its future expectations. The main components of U.S. dollar liquidity include bank reserves, the Federal Reserve's balance sheet, the Treasury's balance, and the balance in the overnight reverse repurchase account. We need to pay attention to changes in these factors, as well as the quarterly refinancing announcements issued by the Treasury Department, to judge the current and future US dollar liquidity situation.
Moreover, the two core factors of employment and prices affect each other to some extent. For example, the Fed will consider both employment and inflation data when deciding whether to cut interest rates or stop shrinking its balance sheet. Therefore, we need to conduct a comprehensive analysis of these data to more accurately predict changes in U.S. dollar liquidity. Through quarterly refinancing announcements and various economic data, we can better understand the operations of the Treasury and the Federal Reserve and thereby predict future trends in U.S. dollar liquidity.
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