(feat) add data pages

pages/data/download_candles/README.md

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+Download historical exchange data as OHLVC candles. Supports multiple trading pairs and custom time ranges/intervals.

pages/data/download_candles/app.py

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+import streamlit as st
+from datetime import datetime, time
+import pandas as pd
+import plotly.graph_objects as go
+
+from frontend.st_utils import initialize_st_page, get_backend_api_client
+
+# Initialize Streamlit page
+initialize_st_page(title="Download Candles", icon="💾")
+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)
+    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"])
+with c3:
+    start_date = st.date_input("Start Date", value=datetime(2023, 1, 1))
+    end_date = st.date_input("End Date", value=datetime(2023, 1, 2))
+with c4:
+    get_data_button = st.button("Get Candles!")
+
+if get_data_button:
+    start_datetime = datetime.combine(start_date, time.min)
+    end_datetime = datetime.combine(end_date, time.max)
+
+    candles = backend_api_client.get_historical_candles(
+        connector=connector,
+        trading_pair=trading_pair,
+        interval=interval,
+        start_time=int(start_datetime.timestamp()) * 1000,
+        end_time=int(end_datetime.timestamp()) * 1000
+    )
+
+    candles_df = pd.DataFrame(candles)
+    candles_df.index = pd.to_datetime(candles_df["timestamp"], unit='s')
+
+    # Plotting the candlestick chart
+    fig = go.Figure(data=[go.Candlestick(
+        x=candles_df.index,
+        open=candles_df['open'],
+        high=candles_df['high'],
+        low=candles_df['low'],
+        close=candles_df['close'],
+        increasing_line_color='#2ECC71',
+        decreasing_line_color='#E74C3C'
+    )])
+    fig.update_layout(
+        height=1000,
+        title="Candlesticks",
+        xaxis_title="Time",
+        yaxis_title="Price",
+        template="plotly_dark",
+        showlegend=False
+    )
+    fig.update_xaxes(rangeslider_visible=False)
+    fig.update_yaxes(title_text="Price")
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Generating CSV and download button
+    csv = candles_df.to_csv(index=False)
+    filename = f"{connector}_{trading_pair}_{start_date.strftime('%Y%m%d')}_{end_date.strftime('%Y%m%d')}.csv"
+    st.download_button(
+        label="Download Candles as CSV",
+        data=csv,
+        file_name=filename,
+        mime='text/csv',
+    )

pages/data/token_spreads/README.md

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+Identify cross-exchange trading opportunities by analyzing differences in token spreads across venues

pages/data/token_spreads/app.py

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+import streamlit as st
+import plotly.express as px
+import CONFIG
+from backend.services.coingecko_client import CoinGeckoClient
+from backend.services.miner_client import MinerClient
+from frontend.st_utils import initialize_st_page
+
+initialize_st_page(title="Token Spreads", icon="🧙")
+
+# Start content here
+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()
+    miner_stats_df = get_miner_stats_df()
+
+miner_coins = coins_df.loc[coins_df["symbol"].isin(miner_stats_df["base"].str.lower().unique()), "name"]
+
+tokens = st.multiselect(
+    "Select the tokens to analyze:",
+    options=coins_df["name"],
+    default=CONFIG.DEFAULT_MINER_COINS
+)
+
+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)
+
+exchanges = st.multiselect(
+    "Select the exchanges to analyze:",
+    options=exchanges_df["name"],
+    default=[exchange for exchange in CONFIG.MINER_EXCHANGES if exchange in exchanges_df["name"].unique()]
+)
+
+height = len(coin_tickers_df["coin_name"].unique()) * 500
+
+fig = px.scatter(
+    data_frame=coin_tickers_df[coin_tickers_df["exchange"].isin(exchanges)],
+    x="volume",
+    y="bid_ask_spread_percentage",
+    color="exchange",
+    log_x=True,
+    log_y=True,
+    facet_col="coin_name",
+    hover_data=["trading_pair"],
+    facet_col_wrap=1,
+    height=height,
+    template="plotly_dark",
+    title="Spread and Volume Chart",
+    labels={
+        "volume": 'Volume (USD)',
+        'bid_ask_spread_percentage': 'Bid Ask Spread (%)'
+    }
+)
+
+# st.write("# Data filters 🏷")
+# st.code("🧳 New filters coming. \nReach us on discord \nif you want to propose one!")
+st.plotly_chart(fig, use_container_width=True)

pages/data/tvl_vs_mcap/README.md

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+Easily compare various DeFi protocols based on their market capitalization and total value locked, using DeFiLlama data.
+
+Data Source: [DefiLlama](https://defillama.com/)

pages/data/tvl_vs_mcap/app.py

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+import numpy as np
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from defillama import DefiLlama
+
+from frontend.st_utils import initialize_st_page
+
+initialize_st_page(title="TVL vs Market Cap", icon="🦉")
+
+# Start content here
+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)
+    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()
+
+default_chains = ["Ethereum", "Solana", "Binance", "Polygon", "Multi-Chain", "Avalanche"]
+
+st.write("### Chains 🔗")
+chains = st.multiselect(
+    "Select the chains to analyze:",
+    options=tvl_mcap_df["chain"].unique(),
+    default=default_chains)
+
+scatter = px.scatter(
+    data_frame=tvl_mcap_df[tvl_mcap_df["chain"].isin(chains)],
+    x="tvl",
+    y="mcap",
+    color="chain",
+    trendline="ols",
+    log_x=True,
+    log_y=True,
+    height=800,
+    hover_data=["name"],
+    template="plotly_dark",
+    title="TVL vs MCAP",
+    labels={
+        "tvl": 'TVL (USD)',
+        'mcap': 'Market Cap (USD)'
+    })
+
+st.plotly_chart(scatter, use_container_width=True)
+
+st.write("---")
+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())
+groupby.append("slug")
+sunburst = px.sunburst(
+    proto_agg,
+    path=groupby,
+    values='tvl',
+    height=800,
+    title="SunBurst",
+    template="plotly_dark",)
+
+st.plotly_chart(sunburst, use_container_width=True)