more python typing

README.md

@@ -72,6 +72,19 @@ This repo is inteded for use with Python 3.9
    python application/trade.py
    ```
 
+Note: If running the command outside of docker, please set the following env var:
+
+   ```
+   export PYTHONPATH="."
+   ```
+
+If running with docker is preferred, we provide the following scripts:
+
+   ```
+   ./scripts/bash/build-docker.sh
+   ./scripts/bash/run-docker-dev.sh
+   ```
+
 ## Architecture
 
 The Polymarket Agents architecture features modular components that can be maintained and extended by individual community members.
@@ -92,19 +105,19 @@ Polymarket Agents connectors standardize data sources and order types.
 
 Files for managing your local environment, server set-up to run the application remotely, and cli for end-user commands.
 
-`Cli.py` is the primary user interface for the repo. Users can run various commands to interact with the Polymarket API, retrieve relevant news articles, query local data, send data/prompts to LLMs, and execute trades in Polymarkets.
+`cli.py` is the primary user interface for the repo. Users can run various commands to interact with the Polymarket API, retrieve relevant news articles, query local data, send data/prompts to LLMs, and execute trades in Polymarkets.
 
 Commands should follow this format:
 
-`python cli.py command_name [attribute value] [attribute value]`
+`python scripts/python/cli.py command_name [attribute value] [attribute value]`
 
 Example:
 
 `get_all_markets`
 Retrieve and display a list of markets from Polymarket, sorted by volume.
 
 ```
-python cli.py get_all_markets --limit <LIMIT> --sort-by <SORT_BY>
+python scripts/python/cli.py get_all_markets --limit <LIMIT> --sort-by <SORT_BY>
 ```
 
 - limit: The number of markets to retrieve (default: 5).

application/creator.py

@@ -2,7 +2,6 @@
 from connectors.gamma import GammaMarketClient as Gamma
 from connectors.polymarket import Polymarket
 
-import pdb
 import time
 
 
@@ -33,7 +32,6 @@ def one_best_market(self):
             print()
             print(f"3. FOUND {len(markets)} MARKETS")
 
-            time.sleep(5)
             print()
             filtered_markets = self.agent.filter_markets(markets)
             print(f"4. FILTERED {len(filtered_markets)} MARKETS")

application/cron.py

@@ -1,4 +1,3 @@
-import datetime as dt
 import time
 from application.trade import Trader
 
@@ -7,21 +6,18 @@
 
 
 class Scheduler:
-    def __init__(self):
+    def __init__(self) -> None:
         self.trader = Trader()
         self.schedule = Scheduler()
 
-    def start(self):
+    def start(self) -> None:
         while True:
             self.schedule.exec_jobs()
             time.sleep(1)
 
 
 class TradingAgent(Scheduler):
-    def __init__(self):
+    def __init__(self) -> None:
         super()
         self.trader = Trader()
         self.weekly(Monday(), self.trader.one_best_trade)
-
-
-# TODO: add task objects for generalized schedule management infrastructure

application/executor.py

@@ -11,13 +11,13 @@
 
 from connectors.gamma import GammaMarketClient as Gamma
 from connectors.chroma import PolymarketRAG as Chroma
-from connectors.objects import SimpleEvent
+from connectors.objects import SimpleEvent, SimpleMarket
 from application.prompts import Prompter
 from connectors.polymarket import Polymarket
 
 
 class Executor:
-    def __init__(self):
+    def __init__(self) -> None:
         load_dotenv()
         self.prompter = Prompter()
         self.openai_api_key = os.getenv("OPENAI_API_KEY")
@@ -56,19 +56,21 @@ def get_polymarket_llm(self, user_input: str) -> str:
         result = self.llm.invoke(messages)
         return result.content
 
-    def filter_events(self, events: "list[SimpleEvent]"):
+    def filter_events(self, events: "list[SimpleEvent]") -> str:
         prompt = self.prompter.filter_events(events)
         result = self.llm.invoke(prompt)
         return result.content
 
-    def filter_events_with_rag(self, events: "list[SimpleEvent]"):
+    def filter_events_with_rag(self, events: "list[SimpleEvent]") -> str:
         prompt = self.prompter.filter_events()
         print()
         print("... prompting ... ", prompt)
         print()
         return self.chroma.events(events, prompt)
 
-    def map_filtered_events_to_markets(self, filtered_events):
+    def map_filtered_events_to_markets(
+        self, filtered_events: "list[SimpleEvent]"
+    ) -> "list[SimpleMarket]":
         markets = []
         for e in filtered_events:
             data = json.loads(e[0].json())
@@ -79,17 +81,14 @@ def map_filtered_events_to_markets(self, filtered_events):
                 markets.append(formatted_market_data)
         return markets
 
-    def filter_markets(self, markets):
+    def filter_markets(self, markets) -> "list[tuple]":
         prompt = self.prompter.filter_markets()
         print()
         print("... prompting ... ", prompt)
         print()
         return self.chroma.markets(markets, prompt)
 
-    def filter_orderbooks(self):
-        pass
-
-    def source_best_trade(self, market_object):
+    def source_best_trade(self, market_object) -> str:
         market_document = market_object[0].dict()
         market = market_document["metadata"]
         outcome_prices = ast.literal_eval(market["outcome_prices"])
@@ -103,15 +102,15 @@ def source_best_trade(self, market_object):
         print()
         result = self.llm.invoke(prompt)
         content = result.content
-        time.sleep(5)
+
         print("result: ", content)
         print()
         prompt = self.prompter.one_best_trade(content, outcomes, outcome_prices)
         print("... prompting ... ", prompt)
         print()
         result = self.llm.invoke(prompt)
         content = result.content
-        time.sleep(5)
+
         print("result: ", content)
         print()
         return content
@@ -123,7 +122,7 @@ def format_trade_prompt_for_execution(self, best_trade: str) -> float:
         usdc_balance = self.polymarket.get_usdc_balance()
         return float(size) * usdc_balance
 
-    def source_best_market_to_create(self, filtered_markets):
+    def source_best_market_to_create(self, filtered_markets) -> str:
         prompt = self.prompter.create_new_market(filtered_markets)
         print()
         print("... prompting ... ", prompt)

application/trade.py

@@ -12,7 +12,7 @@ def __init__(self):
         self.gamma = Gamma()
         self.agent = Agent()
 
-    def one_best_trade(self):
+    def one_best_trade(self) -> None:
         """
 
         one_best_trade is a strategy that evaluates all events, markets, and orderbooks
@@ -33,16 +33,15 @@ def one_best_trade(self):
             print()
             print(f"3. FOUND {len(markets)} MARKETS")
 
-            time.sleep(5)
             print()
             filtered_markets = self.agent.filter_markets(markets)
+            pdb.set_trace()
             print(f"4. FILTERED {len(filtered_markets)} MARKETS")
 
             # TODO: use even more data to build even better models!
             # orderbooks = [self.polymarket.get_orderbooks(m) for m in markets]
             # orderbooks = self.agent.filter_orderbooks()
 
-            time.sleep(5)
             market = filtered_markets[0]
             best_trade = self.agent.source_best_trade(market)
             print(f"5. CALCULATED TRADE {best_trade}")

application/utils.py

@@ -1,7 +1,7 @@
 import json
 
 
-def parse_camel_case(key):
+def parse_camel_case(key) -> str:
     output = ""
     for char in key:
         if char.isupper():
@@ -12,7 +12,7 @@ def parse_camel_case(key):
     return output
 
 
-def preprocess_market_object(market_object):
+def preprocess_market_object(market_object: dict) -> dict:
     description = market_object["description"]
 
     for k, v in market_object.items():
@@ -32,7 +32,7 @@ def preprocess_market_object(market_object):
     return market_object
 
 
-def preprocess_local_json(file_path, preprocessor_function):
+def preprocess_local_json(file_path: str, preprocessor_function: function) -> None:
     with open(file_path, "r+") as open_file:
         data = json.load(open_file)
 

connectors/chroma.py

@@ -20,20 +20,18 @@ def __init__(self, local_db_directory=None, embedding_function=None) -> None:
 
     def load_json_from_local(
         self, json_file_path=None, vector_db_directory="./local_db"
-    ):
+    ) -> None:
         loader = JSONLoader(
             file_path=json_file_path, jq_schema=".[].description", text_content=False
         )
         loaded_docs = loader.load()
 
         embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
-        db2 = Chroma.from_documents(
+        Chroma.from_documents(
             loaded_docs, embedding_function, persist_directory=vector_db_directory
         )
 
-        return db2
-
-    def create_local_markets_rag(self, local_directory="./local_db"):
+    def create_local_markets_rag(self, local_directory="./local_db") -> None:
         all_markets = self.gamma_client.get_all_current_markets()
 
         if not os.path.isdir(local_directory):
@@ -48,15 +46,17 @@ def create_local_markets_rag(self, local_directory="./local_db"):
             json_file_path=local_file_path, vector_db_directory=local_directory
         )
 
-    def query_local_markets_rag(self, local_directory=None, query=None):
+    def query_local_markets_rag(
+        self, local_directory=None, query=None
+    ) -> "list[tuple]":
         embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
         local_db = Chroma(
             persist_directory=local_directory, embedding_function=embedding_function
         )
         response_docs = local_db.similarity_search_with_score(query=query)
         return response_docs
 
-    def events(self, events: "list[SimpleEvent]", prompt: str):
+    def events(self, events: "list[SimpleEvent]", prompt: str) -> "list[tuple]":
         # create local json file
         local_events_directory: str = "./local_db_events"
         if not os.path.isdir(local_events_directory):
@@ -87,12 +87,11 @@ def metadata_func(record: dict, metadata: dict) -> dict:
         local_db = Chroma.from_documents(
             loaded_docs, embedding_function, persist_directory=vector_db_directory
         )
-        # local_db.persist()
 
         # query
         return local_db.similarity_search_with_score(query=prompt)
 
-    def markets(self, markets: "list[SimpleMarket]", prompt: str):
+    def markets(self, markets: "list[SimpleMarket]", prompt: str) -> "list[tuple]":
         # create local json file
         local_events_directory: str = "./local_db_markets"
         if not os.path.isdir(local_events_directory):
@@ -125,7 +124,6 @@ def metadata_func(record: dict, metadata: dict) -> dict:
         local_db = Chroma.from_documents(
             loaded_docs, embedding_function, persist_directory=vector_db_directory
         )
-        # local_db.persist()
 
         # query
         return local_db.similarity_search_with_score(query=prompt)