First steps to using agents

Experiment of using llama 3.2 agent as the decision maker in the trading algorithm

Author: open-source-modelling

Agent.py

@@ -0,0 +1,14 @@
+import ollama
+
+
+def ollama_bigger(value):
+    content = f"Is float {value} bigger than 0? Y or N. Do not write code. Just the correct letter."
+#        print(content)
+    response = ollama.chat(model="llama3.2", messages=[
+    {
+        "role": "user",
+        "content": content
+    },
+    ])
+    return response
+    

MainLoop.py

@@ -3,6 +3,7 @@
 import datetime
 import datetime as dt
 from LiabilityClasses import Liability
+from Agent import ollama_bigger
 
 def create_cashflow_dataframe(cf_dict:dict, unique_dates:list) -> pd.DataFrame:
     """
@@ -178,18 +179,25 @@ def trade(current_date: dt.date, bank_account:pd.DataFrame, eq_units:pd.DataFram
         List with the Dataframe documenting the  number of units after trading and the bank_account DataFrame with the
         updated bank account balance for the modelling date.  
     """
+    
     total_market_value = sum(eq_units[current_date]*eq_price[current_date])+sum(bd_units[current_date]*bd_price[current_date])  # Total value of portfolio after growth
 
-    if total_market_value <= 0:
+    response_MV = ollama_bigger(total_market_value)
+    response_bank = ollama_bigger(bank_account[current_date][0])
+    
+#    if total_market_value <= 0:
+    if response_MV["message"]["content"] == "N" or response_MV["message"]["content"] == "N.":
         pass
-    elif bank_account[current_date][0] < 0:  # Sell assets
+#    elif bank_account[current_date][0] < 0:  # Sell assets
+    elif response_bank["message"]["content"] == "N" or response_bank["message"]["content"] == "N.":  # Sell assets
         percent_to_sell = min(1, -bank_account[current_date][0] / total_market_value)  # How much of the portfolio needs to be sold
         eq_units[current_date] = eq_units[current_date] * (1 - percent_to_sell) 
         bd_units[current_date] = bd_units[current_date] * (1 - percent_to_sell)
 
         bank_account[current_date] += total_market_value - sum(eq_units[current_date]*eq_price[current_date])-sum(bd_units[current_date]*bd_price[current_date])  # Add cash to bank account equal to shares sold
 
-    elif bank_account[current_date][0] > 0:  # Buy assets
+#    elif bank_account[current_date][0] > 0:  # Buy assets
+    elif response_bank["message"]["content"] =="Y" or response_bank["message"]["content"] =="Y.":  # Buy assets
         percent_to_buy = bank_account[current_date][0] / total_market_value  # What % of the portfolio is the excess cash
         eq_units[current_date] = eq_units[current_date] * (1 + percent_to_buy)  
         bd_units[current_date] = bd_units[current_date] * (1 + percent_to_buy)