- In this folder, read the round4_wiki.md carefully to understand the algorithm trading task and read the Trader_class.md and overall.py to understand the format for writing the strategy. Update the Trade Class in overall.py to satisfy the Round4 requirement based on the wiki; to for example, check if we have the trader self.buyer = buyer attribute and stuff. Build the participant registry, use the day 1, 2,3 trade and order data stored in .csv file in ROUND_4 folder. 1. Parse all the trade, extract all unique buyer/seller names, count their trade frequency and volume per product. 2. Compute per-participant markout at τ={10,50,200}\tau = \{10, 5, 200\}τ={10,50,200} ticks unless you have better evaluation approach. 3. Rank participants by informativeness. Implement weighted OFI using informativeness scores as weights. Visualize whenever you can and return me a Jupyter Notebook file named trader_id_analyse.ipynb.
trader analysis result
- read the trader_analysis_result.md to see how the analysis can lead to a profitable algo strategy, think carefully about how we should follow the informed traders and market maker’s behavior and when to exit. Following the structure and constraints (such as inventory constraints) in Trader_class.md and round4_wiki.md , generating an algorithm strategy from the idea of following and fading described in trader_analysis_result. md, do not analysis any price patterns in ROUND_4 folder; this version of strategy should focus only on how we can make profit from identifying the traders’ behavior. Return me a .py named round4_traderstrat_v1.
Round 4 Trader-Identity Strategy v2 — Plan
- In this folder, read the round4_wiki.md carefully to understand the algorithm trading task and read the Trader_class.md and overall.py to understand the format for writing the strategy. The trade and order data are stored in .csv file in folder ROUND_4, day 1, 2, 3. Read the trader_analyse_result.md to understand the trader behavior, and analyse the behavior and build potential algorithm signal, such as pure event-driven momentum and catogrized by product; do not use WOFI since there is no leap and lag. return me a Python file with the strategy named round4_trader_v1.py