Separate out functions to separate file

prediction_market_agent/agents/microchain_agent/functions.py

@@ -0,0 +1,223 @@
+import pprint
+
+from microchain import Function
+from prediction_market_agent_tooling.markets.agent_market import (
+    AgentMarket,
+    FilterBy,
+    SortBy,
+)
+from prediction_market_agent_tooling.markets.omen.omen import OmenAgentMarket
+
+balance = 50
+outcomeTokens = {}
+outcomeTokens["Will Joe Biden get reelected in 2024?"] = {"yes": 0, "no": 0}
+outcomeTokens["Will Bitcoin hit 100k in 2024?"] = {"yes": 0, "no": 0}
+
+
+class Sum(Function):
+    @property
+    def description(self):
+        return "Use this function to compute the sum of two numbers"
+
+    @property
+    def example_args(self):
+        return [2, 2]
+
+    def __call__(self, a: float, b: float):
+        return a + b
+
+
+class Product(Function):
+    @property
+    def description(self):
+        return "Use this function to compute the product of two numbers"
+
+    @property
+    def example_args(self):
+        return [2, 2]
+
+    def __call__(self, a: float, b: float):
+        return a * b
+
+
+class GetMarkets(Function):
+    @property
+    def description(self):
+        return "Use this function to get a list of predction markets and the current yes prices"
+
+    @property
+    def example_args(self):
+        return []
+
+    def __call__(self):
+        # Get the 5 markets that are closing soonest
+        markets: list[AgentMarket] = OmenAgentMarket.get_binary_markets(
+            filter_by=FilterBy.OPEN,
+            sort_by=SortBy.CLOSING_SOONEST,
+            limit=5,
+        )
+
+        market_questions_and_prices = []
+        for market in markets:
+            market_questions_and_prices.append(market.question)
+            market_questions_and_prices.append(market.p_yes)
+        return market_questions_and_prices
+
+
+class GetPropabilityForQuestion(Function):
+    @property
+    def description(self):
+        return "Use this function to research the probability of an event occuring"
+
+    @property
+    def example_args(self):
+        return ["Will Joe Biden get reelected in 2024?"]
+
+    def __call__(self, a: str):
+        if a == "Will Joe Biden get reelected in 2024?":
+            return 0.41
+        if a == "Will Bitcoin hit 100k in 2024?":
+            return 0.22
+
+        return 0.0
+
+
+class GetBalance(Function):
+    @property
+    def description(self):
+        return "Use this function to get your own balance in $"
+
+    @property
+    def example_args(self):
+        return []
+
+    def __call__(self):
+        print(f"Your balance is: {balance} and ")
+        pprint.pprint(outcomeTokens)
+        return balance
+
+
+class BuyYes(Function):
+    @property
+    def description(self):
+        return "Use this function to buy yes outcome tokens of a prediction market. The second parameter specifies how much $ you spend."
+
+    @property
+    def example_args(self):
+        return ["Will Joe Biden get reelected in 2024?", 2]
+
+    def __call__(self, market: str, amount: int):
+        global balance
+        if amount > balance:
+            return (
+                f"Your balance of {balance} $ is not large enough to spend {amount} $."
+            )
+
+        balance -= amount
+        return "Bought " + str(amount * 2) + " yes outcome token of: " + market
+
+
+class BuyNo(Function):
+    @property
+    def description(self):
+        return "Use this function to buy no outcome tokens of a prdiction market. The second parameter specifies how much $ you spend."
+
+    @property
+    def example_args(self):
+        return ["Will Joe Biden get reelected in 2024?", 4]
+
+    def __call__(self, market: str, amount: int):
+        global balance
+        if amount > balance:
+            return (
+                f"Your balance of {balance} $ is not large enough to spend {amount} $."
+            )
+
+        balance -= amount
+        return "Bought " + str(amount * 2) + " no outcome token of: " + market
+
+
+class SellYes(Function):
+    @property
+    def description(self):
+        return "Use this function to sell yes outcome tokens of a prediction market. The second parameter specifies how much tokens you sell."
+
+    @property
+    def example_args(self):
+        return ["Will Joe Biden get reelected in 2024?", 2]
+
+    def __call__(self, market: str, amount: int):
+        global outcomeTokens
+        if amount > outcomeTokens[market]["yes"]:
+            return f"Your balance of {outcomeTokens[market]['yes']} yes outcome tokens is not large enough to sell {amount}."
+
+        outcomeTokens[market]["yes"] -= amount
+        return "Sold " + str(amount) + " yes outcome token of: " + market
+
+
+class SellNo(Function):
+    @property
+    def description(self):
+        return "Use this function to sell no outcome tokens of a prdiction market. The second parameter specifies how much tokens you sell."
+
+    @property
+    def example_args(self):
+        return ["Will Joe Biden get reelected in 2024?", 4]
+
+    def __call__(self, market: str, amount: int):
+        global outcomeTokens
+        if amount > outcomeTokens[market]["no"]:
+            return f"Your balance of {outcomeTokens[market]['no']} no outcome tokens is not large enough to sell {amount}."
+
+        outcomeTokens[market]["no"] -= amount
+        return "Sold " + str(amount) + " no outcome token of: " + market
+
+
+class BalanceToOutcomes(Function):
+    @property
+    def description(self):
+        return "Use this function to convert your balance into equal units of 'yes' and 'no' outcome tokens. The function takes the amount of balance as the argument."
+
+    @property
+    def example_args(self):
+        return ["Will Joe Biden get reelected in 2024?", 50]
+
+    def __call__(self, market: str, amount: int):
+        global balance
+        global outcomeTokens
+        outcomeTokens[market]["yes"] += amount
+        outcomeTokens[market]["no"] += amount
+        balance -= amount
+        return f"Converted {amount} units of balance into {amount} 'yes' outcome tokens and {amount} 'no' outcome tokens. Remaining balance is {balance}."
+
+
+class SummarizeLearning(Function):
+    @property
+    def description(self):
+        return "Use this function summarize your learnings and save them so that you can access them later."
+
+    @property
+    def example_args(self):
+        return [
+            "Today I learned that I need to check my balance fore making decisions about how much to invest."
+        ]
+
+    def __call__(self, summary: str):
+        # print(summary)
+        # pprint.pprint(outcomeTokens)
+        return summary
+
+
+ALL_FUNCTIONS = [
+    Sum,
+    Product,
+    GetMarkets,
+    GetPropabilityForQuestion,
+    GetBalance,
+    BuyYes,
+    BuyNo,
+    SellYes,
+    SellNo,
+    # BalanceToOutcomes,
+    SummarizeLearning,
+]