- Active strategy¶
Buying and selling assets based on the market movement. Differs from buy-and-hold by actively (hourly, daily, weekly) engaging in trading. Read more.
Automated market maker (AMM) is a bonding curve based decentralised exchange. It does not have an order book.
Apache Arrow is a popular open source in-memory analytics technology kit. More information.
- Autonomous agent¶
An agent software that acts without human intervention. Once started, there is no further need for system administration or othe work.
Simulating the efficiency of a trading strategy against historical data.
An old Python based framework for strategy backtesting and live execution. See documentation.
- Base token¶
The token you want to buy or sell in a trading pair. See also quote token.
The (time) bucket to a time period for candle data. For example, you can have one minute, one hour or time buckets, describing for the what period of a time the candle includes the trades.
Also known as fork. A product launched based on the open source code of another existing product. In the context of on-chain, usually hostile to the original product and competes from the same liquidity.
Standard Python way to annotate data structures. More information.
A data bundle consisting of candles or other quantitative data sources. The most usual dataset is hourly or daily candles for multiple assets, distributed as a downloadable archive of several hundreds of megabytes.
- Dataset server¶
- Decentralised exchange¶
An exchange where all trades happen purely on-chain. These exchanges are public, fair, cheap and especially censorship proof. There is no middleman like a broker when you are trading on these venues, but you get equal access to the trade flow. Decentralised exchanges can be AMM based or order book based. Some of the most popular decentralised exchanges are Uniswap, Sushiwap and PancakeSwap.
- Directional strategy¶
A trading strategy where you bet the market to go up or down.
How many % the asset can go down. Read more.
The risk of a strategy for the volatility of a particular asset. For example, if you have 100% exposure to ETH and ETH prices drops to zero, you lose all of your money.
Fastquant allows you to easily backtest investment strategies with as few as three lines of Python code. Its goal is to promote data driven investments by making quantitative analysis in finance accessible to everyone. Fastquant builds on the top of Backtrader. See Github repository.
- High-frequency trading¶
High-frequency trading, or HFT for short, is a trading strategy where you do arbitration, cross-market market making or such and compete against the other actors with your technical speed. Trading Strategy is not suitable framework for HFT trading, though its data can aid to come up with good HFT strategiees.
- Jupyter notebook¶
A popular Python based data science tool. Jupyter allows users to run data research notebooks interactively. Jupyter notebooks can be easily shared, run on your local computer or on a hosted cloud environment, both free and paid. More information.
Liquidity refers to the depth of the order books: how much volume a single trade can achieve without moving the price. It can be expressed as slippage or absolute depth of the order book. The latter is very easy for AMM based exchanges where the liquidity is a continuous function. Trading Strategy provides datasets for AMM liquidity in
- Market neutral strategy¶
Market neutral strategies are trading strategies that have little or no exposure to crypto asset volatility. They are often high-frequency trading strategies, like arbitrage. Good market neutral strategies can make 10% - 20% monthly yield in cryptocurrency markets.
A smart contract based service model where the owner of the assets never lose the control of the assets. This is opposite to most traditional finance services where you cannot see what happens to your money after the deposit or whether you are able to withdraw. The integrity of the service provider in the traditional finance thus needs to be guaranteted through regulation or government bailouts. The non-custodial model is specific to smart contracts and cannot be achieved without a blockchain. Read more.
Notebook refers to an interactively editable Python script or application, mixed with diagrams and notes. The format was popularised by Jupyter notebook.
A typical candle contains open, high, low and close price and trade volume for a time bucket. Because on-chain exposes more data than centralised exchanges, Trading Strategy data also contains individual buys and sells, US dollar exchange rate and so forth.
On-chain refers to any activity that happens purely on a public blockchain. It means the data and trading venues are publicly and fairly available for anyone.
A popular Python based data analysis library. More information.
A popular file format for large datasets from Apache Arrow project. More information.
- Pine Script¶
- Private strategy¶
A trading strategy where the source code of the strategy is not disclosed to public. Private strategies can still be non-custodial and enjoy the benefits of Trading Strategy protocol trade execution and fee distribution. :ref`Read more <Private strategies>`.
- Quote token¶
The token that acts as a nominator for the price when you are buying or selling. Usually this is more well-known token of the pair: ETH, BTC or any of various USD stablecoins. See also base token.
- Risk-free rate¶
The expected return for the money that is considered (almost) risk-free. On the traditional markets, this is the tresury note or government bond yield (although you still have some risks like the sovereignity risk). In DeFi this is considered an US dollar lending pool rate, like one you would get from Aave USDC pool.
- Smart contract¶
An automated transactional service running on any of blockchains supporting smart contracts. Typically runs on Ethereum based blockchain and is written in Solidity programming language.
Also known as trading strategy or algorithm. The trading strategy is the rulebook what trades to make an how. In the context of quantative finance, and especially automated trading, this rulebook can be expressed as an algorithm and trading bot that has programmed rules for every situation the strategy may encounter.
- Technical analysis¶
Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities by analyzing statistical trends gathered from trading activity, such as price movement and volume. More information.
Trading view is the world most ppopular trading strategy platform. It lets you discover investment ideas and showcase your talents to a large and active community of traders. Easy and intuitive for beginners, and powerful enough for advanced chartists. Trading View has all the charting tools you need to share and view trading ideas. Real-time data and browser-based charts let you do your research from anywhere, since there are no installations or complex setups. Read more.
The most popular AMM based decentralised exchange. Uniswap has two major versios. In version 2 (v2) the liquidity is evenly distributed across the bonding curve. In version 3, the liquidity providers can have liquidity on a partial curve, simulate order book and have better capital efficiency. Most decentralised exchanges are Uniswap v2 clones.
A popular US cash and US treasury note backed stablecoin from Circle. Read more.
- Yield farming¶
Pooling assets of multiple people for passive trading strategies. Usually yield farming pools rely on liquidity mining token distribution which they immediately sell (auto compounding). Yield farms operate solely on smart contracts and their strategies are limited. Yield farms almost always take zero market risk agains their quote token.