(feat) add portfolio page

frontend/pages/orchestration/portfolio/README.md

@@ -0,0 +1,19 @@
+### Description
+
+This page helps you deploy and manage Hummingbot instances:
+
+- Starting and stopping Hummingbot Broker
+- Creating, starting and stopping bot instances
+- Managing strategy and script files that instances run
+- Fetching status of running instances
+
+### Maintainers
+
+This page is maintained by Hummingbot Foundation as a template other pages:
+
+* [cardosfede](https://github.com/cardosfede)
+* [fengtality](https://github.com/fengtality)
+
+### Wiki
+
+See the [wiki](https://github.com/hummingbot/dashboard/wiki/%F0%9F%90%99-Bot-Orchestration) for more information.

frontend/pages/orchestration/portfolio/app.py

@@ -0,0 +1,58 @@
+from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
+from backend.services.backend_api_client import BackendAPIClient
+from frontend.st_utils import initialize_st_page
+import streamlit as st
+import pandas as pd
+
+initialize_st_page(title="Portfolio", icon="💰")
+
+# Page content
+client = BackendAPIClient.get_instance(host=BACKEND_API_HOST, port=BACKEND_API_PORT)
+NUM_COLUMNS = 4
+
+@st.cache_data
+def get_all_balances():
+    return client.get_all_balances()
+
+# Fetch all balances
+balances = get_all_balances()
+
+# Convert balances to a DataFrame for easier manipulation
+def balances_to_df(balances):
+    data = []
+    for account, exchanges in balances.items():
+        for exchange, tokens in exchanges.items():
+            for token, amount in tokens.items():
+                data.append({"Account": account, "Exchange": exchange, "Token": token, "Amount": amount})
+    return pd.DataFrame(data)
+
+df_balances = balances_to_df(balances)
+c1, c2 = st.columns([1, 1])
+with c1:
+    st.header("Current Balances")
+with c2:
+    st.header("Aggregated Balances")
+
+c1, c2, c3, c4 = st.columns([2.5, 1.5, 1.5, 1.1])
+with c1:
+    # Display balances
+    st.subheader("All Balances")
+    st.dataframe(df_balances)
+
+with c2:
+    # Aggregation at the account level
+    account_agg = df_balances.groupby(["Account", "Token"])["Amount"].sum().reset_index()
+    st.subheader("Account Level")
+    st.dataframe(account_agg)
+
+with c3:
+    # Aggregation at the exchange level
+    exchange_agg = df_balances.groupby(["Exchange", "Token"])["Amount"].sum().reset_index()
+    st.subheader("Exchange Level")
+    st.dataframe(exchange_agg)
+
+with c4:
+    # Overall holdings
+    overall_agg = df_balances.groupby("Token")["Amount"].sum().reset_index()
+    st.subheader("Token Level")
+    st.write(overall_agg)