Add transaction metrics and visualizations

app.py

@@ -18,52 +18,116 @@ def process_transactions(data):
     # Convert the data into a pandas DataFrame for easy manipulation
     rows = []
     for tx in transactions:
+        # Normalize amounts
+        sending_amount = float(tx["sending"]["amount"]) / (10 ** tx["sending"]["token"]["decimals"])
+        receiving_amount = float(tx["receiving"]["amount"]) / (10 ** tx["receiving"]["token"]["decimals"])
+
+        # Convert timestamps to datetime objects
+        sending_timestamp = datetime.utcfromtimestamp(tx["sending"]["timestamp"])
+        receiving_timestamp = datetime.utcfromtimestamp(tx["receiving"]["timestamp"])
+
+        # Prepare row data
         rows.append({
             "transactionId": tx["transactionId"],
-            "from": tx["fromAddress"],
-            "to": tx["toAddress"],
-            "token_symbol": tx["sending"]["token"]["symbol"],
-            "amount": float(tx["sending"]["amount"]) / (10 ** tx["sending"]["token"]["decimals"]),  # Normalize the amount
-            "amount_usd": float(tx["sending"]["amountUSD"]),
-            "gas_used": tx["sending"]["gasUsed"],
-            "timestamp": datetime.utcfromtimestamp(tx["sending"]["timestamp"]).strftime('%Y-%m-%d %H:%M:%S'),
-            "week": pd.to_datetime(datetime.utcfromtimestamp(tx["sending"]["timestamp"])).strftime('%Y-%W')  # Group by week
+            "from_address": tx["fromAddress"],
+            "to_address": tx["toAddress"],
+            "sending_chain": tx["sending"]["chainId"],
+            "receiving_chain": tx["receiving"]["chainId"],
+            "sending_token_symbol": tx["sending"]["token"]["symbol"],
+            "receiving_token_symbol": tx["receiving"]["token"]["symbol"],
+            "sending_amount": sending_amount,
+            "receiving_amount": receiving_amount,
+            "sending_amount_usd": float(tx["sending"]["amountUSD"]),
+            "receiving_amount_usd": float(tx["receiving"]["amountUSD"]),
+            "sending_gas_used": int(tx["sending"]["gasUsed"]),
+            "receiving_gas_used": int(tx["receiving"]["gasUsed"]),
+            "sending_timestamp": sending_timestamp,
+            "receiving_timestamp": receiving_timestamp,
+            "date": sending_timestamp.date(),  # Group by day
+            "week": sending_timestamp.strftime('%Y-%W')  # Group by week
         })
     
     df = pd.DataFrame(rows)
     return df
 
-# Function to create week-over-week visualizations
-def create_wow_visualizations():
+# Function to create visualizations based on the metrics
+def create_visualizations():
     transactions_data = fetch_transactions()
     df = process_transactions(transactions_data)
 
-    # Aggregate the data by week
-    weekly_data = df.groupby("week").agg({
-        "amount_usd": "sum",  # Total USD amount
-        "gas_used": "sum"     # Total gas used
-    }).reset_index()
+    # Ensure that chain IDs are strings for consistent grouping
+    df["sending_chain"] = df["sending_chain"].astype(str)
+    df["receiving_chain"] = df["receiving_chain"].astype(str)
+
+    # 1. Number of Transactions per Chain per Day per Agent
+    tx_per_chain_agent = df.groupby(["date", "from_address", "sending_chain"]).size().reset_index(name="transaction_count")
+    fig_tx_chain_agent = px.bar(tx_per_chain_agent, x="date", y="transaction_count", color="sending_chain", barmode="group",
+                                facet_col="from_address", title="Number of Transactions per Chain per Agent per Day")
+
+    # 2. Number of Opportunities Taken per Agent per Day
+    opportunities_per_agent = df.groupby(["date", "from_address"]).size().reset_index(name="opportunities_taken")
+    fig_opportunities_agent = px.bar(opportunities_per_agent, x="date", y="opportunities_taken", color="from_address",
+                                     title="Number of Opportunities Taken per Agent per Day")
 
-    # Plot: Total Transaction Amounts by Week
-    fig_amount = px.bar(weekly_data, x="week", y="amount_usd", title="Weekly Transaction Amounts in USD")
+    # 3. Amount of Investment in Pools Daily per Agent (Note: Assuming sending_amount_usd as investment)
+    # Since we might not have explicit data about pool investments, we'll use sending_amount_usd
+    investment_per_agent = df.groupby(["date", "from_address"])["sending_amount_usd"].sum().reset_index()
+    fig_investment_agent = px.bar(investment_per_agent, x="date", y="sending_amount_usd", color="from_address",
+                                  title="Amount of Investment (USD) per Agent per Day")
 
-    # Plot: Total Gas Used by Week
-    fig_gas = px.line(weekly_data, x="week", y="gas_used", title="Weekly Gas Used")
+    # 4. Number of Swaps per Day
+    # Assuming each transaction is a swap if sending and receiving tokens are different
+    df["is_swap"] = df.apply(lambda x: x["sending_token_symbol"] != x["receiving_token_symbol"], axis=1)
+    swaps_per_day = df[df["is_swap"]].groupby("date").size().reset_index(name="swap_count")
+    fig_swaps_per_day = px.bar(swaps_per_day, x="date", y="swap_count", title="Number of Swaps per Day")
 
-    return fig_amount, fig_gas
+    # 5. Aggregated Metrics over All Traders
+    amount_usd = df["sending_amount_usd"]
+    stats = {
+        "Total": amount_usd.sum(),
+        "Average": amount_usd.mean(),
+        "Min": amount_usd.min(),
+        "Max": amount_usd.max(),
+        "25th Percentile": amount_usd.quantile(0.25),
+        "50th Percentile (Median)": amount_usd.median(),
+        "75th Percentile": amount_usd.quantile(0.75),
+    }
+    stats_df = pd.DataFrame(list(stats.items()), columns=["Metric", "Value"])
+
+    # Visualization for Aggregated Metrics
+    fig_stats = px.bar(stats_df, x="Metric", y="Value", title="Aggregated Transaction Amount Metrics (USD)")
+
+    return fig_tx_chain_agent, fig_opportunities_agent, fig_investment_agent, fig_swaps_per_day, fig_stats
 
 # Gradio interface
 def dashboard():
     with gr.Blocks() as demo:
-        gr.Markdown("# Valory Transactions Dashboard (Week-over-Week)")
+        gr.Markdown("# Valory Transactions Dashboard")
 
         # Fetch and display visualizations
-        fig_amount, fig_gas = create_wow_visualizations()
-        gr.Plot(fig_amount)
-        gr.Plot(fig_gas)
+        with gr.Tab("Transactions per Chain per Agent"):
+            fig_tx_chain_agent, _, _, _, _ = create_visualizations()
+            gr.Plot(fig_tx_chain_agent)
+        
+        with gr.Tab("Opportunities per Agent"):
+            _, fig_opportunities_agent, _, _, _ = create_visualizations()
+            gr.Plot(fig_opportunities_agent)
+        
+        with gr.Tab("Investment per Agent"):
+            _, _, fig_investment_agent, _, _ = create_visualizations()
+            gr.Plot(fig_investment_agent)
+        
+        with gr.Tab("Swaps per Day"):
+            _, _, _, fig_swaps_per_day, _ = create_visualizations()
+            gr.Plot(fig_swaps_per_day)
+        
+        with gr.Tab("Aggregated Metrics"):
+            _, _, _, _, fig_stats = create_visualizations()
+            gr.Plot(fig_stats)
     
     return demo
 
 # Launch the dashboard
 if __name__ == "__main__":
     dashboard().launch()
+