(feat) update data

pages/data/download_candles/app.py

@@ -1,9 +1,10 @@
-import streamlit as st
 from datetime import datetime, time
+
 import pandas as pd
 import plotly.graph_objects as go
+import streamlit as st
 
-from frontend.st_utils import initialize_st_page, get_backend_api_client
+from frontend.st_utils import get_backend_api_client, initialize_st_page
 
 # Initialize Streamlit page
 initialize_st_page(title="Download Candles", icon="💾")
@@ -11,7 +12,9 @@ backend_api_client = get_backend_api_client()
 
 c1, c2, c3, c4 = st.columns([2, 2, 2, 0.5])
 with c1:
-    connector = st.selectbox("Exchange", ["binance_perpetual", "binance", "gate_io", "gate_io_perpetual", "kucoin", "ascend_ex"], index=0)
+    connector = st.selectbox("Exchange",
+                             ["binance_perpetual", "binance", "gate_io", "gate_io_perpetual", "kucoin", "ascend_ex"],
+                             index=0)
     trading_pair = st.text_input("Trading Pair", value="BTC-USDT")
 with c2:
     interval = st.selectbox("Interval", options=["1m", "3m", "5m", "15m", "1h", "4h", "1d", "1s"])
@@ -29,8 +32,8 @@ if get_data_button:
         connector=connector,
         trading_pair=trading_pair,
         interval=interval,
-        start_time=int(start_datetime.timestamp()) * 1000,
+        start_time=int(start_datetime.timestamp()),
-        end_time=int(end_datetime.timestamp()) * 1000
+        end_time=int(end_datetime.timestamp())
     )
 
     candles_df = pd.DataFrame(candles)

pages/data/token_spreads/app.py

@@ -1,5 +1,6 @@
-import streamlit as st
 import plotly.express as px
+import streamlit as st
+
 import CONFIG
 from backend.services.coingecko_client import CoinGeckoClient
 from backend.services.miner_client import MinerClient
@@ -11,22 +12,27 @@ initialize_st_page(title="Token Spreads", icon="🧙")
 cg_utils = CoinGeckoClient()
 miner_utils = MinerClient()
 
+
 @st.cache_data
 def get_all_coins_df():
     return cg_utils.get_all_coins_df()
 
+
 @st.cache_data
 def get_all_exchanges_df():
     return cg_utils.get_all_exchanges_df()
 
+
 @st.cache_data
 def get_miner_stats_df():
     return miner_utils.get_miner_stats_df()
 
+
 @st.cache_data
 def get_coin_tickers_by_id_list(coins_id: list):
     return cg_utils.get_coin_tickers_by_id_list(coins_id)
 
+
 with st.spinner(text='In progress'):
     exchanges_df = get_all_exchanges_df()
     coins_df = get_all_coins_df()
@@ -43,7 +49,8 @@ tokens = st.multiselect(
 coins_id = coins_df.loc[coins_df["name"].isin(tokens), "id"].tolist()
 
 coin_tickers_df = get_coin_tickers_by_id_list(coins_id)
-coin_tickers_df["coin_name"] = coin_tickers_df.apply(lambda x: coins_df.loc[coins_df["id"] == x.token_id, "name"].item(), axis=1)
+coin_tickers_df["coin_name"] = coin_tickers_df.apply(
+    lambda x: coins_df.loc[coins_df["id"] == x.token_id, "name"].item(), axis=1)
 
 exchanges = st.multiselect(
     "Select the exchanges to analyze:",

pages/data/tvl_vs_mcap/app.py

@@ -1,7 +1,7 @@
 import numpy as np
-import streamlit as st
 import pandas as pd
 import plotly.express as px
+import streamlit as st
 from defillama import DefiLlama
 
 from frontend.st_utils import initialize_st_page
@@ -12,16 +12,21 @@ initialize_st_page(title="TVL vs Market Cap", icon="🦉")
 MIN_TVL = 1000000.
 MIN_MCAP = 1000000.
 
+
 @st.cache_data
 def get_tvl_mcap_data():
     llama = DefiLlama()
     df = pd.DataFrame(llama.get_all_protocols())
-    tvl_mcap_df = df.loc[(df["tvl"]>0) & (df["mcap"]>0), ["name", "tvl", "mcap", "chain", "category", "slug"]].sort_values(by=["mcap"], ascending=False)
+    tvl_mcap_df = df.loc[
-    return tvl_mcap_df[(tvl_mcap_df["tvl"] > MIN_TVL) & (tvl_mcap_df["mcap"]> MIN_MCAP)]
+        (df["tvl"] > 0) & (df["mcap"] > 0), ["name", "tvl", "mcap", "chain", "category", "slug"]].sort_values(
+        by=["mcap"], ascending=False)
+    return tvl_mcap_df[(tvl_mcap_df["tvl"] > MIN_TVL) & (tvl_mcap_df["mcap"] > MIN_MCAP)]
+
 
 def get_protocols_by_chain_category(protocols: pd.DataFrame, group_by: list, nth: list):
     return protocols.sort_values('tvl', ascending=False).groupby(group_by).nth(nth).reset_index()
 
+
 with st.spinner(text='In progress'):
     tvl_mcap_df = get_tvl_mcap_data()
 
@@ -57,7 +62,8 @@ st.write("### SunBurst 🌞")
 groupby = st.selectbox('Group by:', [['chain', 'category'], ['category', 'chain']])
 nth = st.slider('Top protocols by Category', min_value=1, max_value=5)
 
-proto_agg = get_protocols_by_chain_category(tvl_mcap_df[tvl_mcap_df["chain"].isin(chains)], groupby, np.arange(0, nth, 1).tolist())
+proto_agg = get_protocols_by_chain_category(tvl_mcap_df[tvl_mcap_df["chain"].isin(chains)],
+                                            groupby, np.arange(0, nth, 1).tolist())
 groupby.append("slug")
 sunburst = px.sunburst(
     proto_agg,
@@ -65,6 +71,6 @@ sunburst = px.sunburst(
     values='tvl',
     height=800,
     title="SunBurst",
-    template="plotly_dark",)
+    template="plotly_dark", )
 
-st.plotly_chart(sunburst, use_container_width=True)
+st.plotly_chart(sunburst, use_container_width=True)