Botcamp Cohort #2 Demo Day Recap
We recently finished Cohort #2 for Hummingbot Botcamp, our developer bootcamp that teaches anyone how to create custom algo trading scripts with Hummingbot.
Students presented their strategies during Demo Day, the final live session of Botcamp and competed for pool of HBOT rewards.
Our last cohort was composed of people from US, Europe, Asia & Australia. From developers, quant traders and startup founders, all the students joined Hummingbot to learn the theory and practice of developing algo trading scripts.
We are extremely proud of all the hard work that our students from Cohort #2 put on creating their strategies, and the amazing results they achieved. They demonstrated their skills, creativity and ingenuity in creating cutting-edge scripts.
During the last Demo Day, each student presented their strategies to an audience of mentors, students and team members that joined the session. After all the students showcased their work, they had the opportunity to vote for the best scripts to have a chance to win HBOT rewards! The students demonstrated not only their technical skills but also their ability to communicate complex ideas in a clear and concise manner.
Watch the highlights from the Final Live Session from Cohort #2 here: https://www.youtube.com/watch?v=C8SADS3LUak
Certified Scripts
Below are the scripts that Botcamp students created with the help of their mentors and presented during Cohort #2 Demo Day. We reviewed and accepted each of these scripts beforehand. Congratulations to all the students and mentors for all their hard work!
microprice_calculator.py
Nathan is a student that built a Hummingbot script that computes the microprice using order book data.
To learn more about micro-price, Sasha Stoikov, legendary quant researcher and co-author of the seminal paper on market making upon which our Avellaneda strategy is based, explained more about micro-price in his lecture where he also featured Hummingbot: learn more here.
Nathan's script will search for data of the trading pair and exchange, and if enough data is not found, the script will begin recording data to access. Once the script computes microprice adjustments, it will compute and display the current midprice, adjusted for the imbalance of the order book, and other information.
flexible_savings.py
Jaanus submitted a strategy to leverage Flexible Savings on Binance exchange while employing a low-frequency directional strategy. Since low-frequency directional strategies involve inactive assets in a spot wallet most of the time, we can earn interest through flexible savings by storing these assets in a savings account. The assets can be withdrawn instantly as the term “flexible” means they are not locked.
His script implemented a directional strategy called the Four-Week Rule, and involves opening a long position if the closing price is above the previous four week highs and opening a short position if it is below the previous four week lows. The script uses a two week moving average to liquidate positions as the candle closing price crosses it. Additionally, the script transfers assets from a spot account to a flexible savings account if the exchange supports it. Whenever opening or closing a position, the assets are temporarily transferred back to the spot account, allowing the strategy to earn additional income from the interest accrued in the savings account.
liquidity_mining_optimal_price_example.py
Alexsandrs designed a strategy that will figure out an optimal price based on the historical max trade volume and bid/ask entries. The goal is to place buy and sell orders that will not get filled. which will increase the chances of getting higher rewards for liquidity mining.
Current PMM and Liquidity Mining strategies don't allow to dynamically control spreads/prices of users' orders - users either have to manually calculate and adjust the spreads/prices or have to use Order Optimization, and have to decide what depth amount should be used, and it will be different for each coin.
This script will fetch the historical market trade, figure out the max trade volume and based on this volume it will find out an optimal price using the current order book data.
These are only some of the many custom strategies that students built in Botcamp. Their dedication is inspiring and we can't wait to see what they achieve in the future!
We want to congratulate all the students of Botcamp Cohort #2! We are confident that they will go on to do great things and we are excited to see where the journey takes them!