(feat) first version of strategy performance

pages/7_🚀_Strategy_Performance.py

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+import os
+import sqlite3
+from datetime import datetime
+
+import ccxt
+import numpy as np
+
+import pandas as pd
+import streamlit as st
+
+from utils.data_manipulation import BotData
+from utils.graphs import CandlesGraph
+
+st.set_page_config(
+    page_title="Hummingbot Dashboard",
+    page_icon=":bar_chart:",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+st.title("🚀 Strategy Performance")
+
+intervals = {
+    "1m": 60,
+    "3m": 60 * 3,
+    "30m": 60 * 30,
+    "1h": 60 * 60,
+    "6h": 60 * 60 * 6,
+    "1d": 60 * 60 * 24,
+
+}
+
+
+@st.cache_resource
+def get_data(db_name: str):
+    path = os.path.join("data", db_name)
+    conn = sqlite3.connect(path)
+    order_df = pd.read_sql_query("SELECT * FROM 'Order'", conn)
+    order_status_df = pd.read_sql_query("SELECT * FROM OrderStatus", conn)
+    trade_fill_df = pd.read_sql_query("SELECT * FROM TradeFill", conn)
+    order_df['creation_timestamp'] = pd.to_datetime(order_df['creation_timestamp'], unit="ms")
+    order_df['last_update_timestamp'] = pd.to_datetime(order_df['last_update_timestamp'], unit="ms")
+    trade_fill_df["timestamp"] = pd.to_datetime(trade_fill_df["timestamp"], unit="ms")
+    # TODO: GitHub issue #8
+    trade_fill_df["price"] = trade_fill_df["price"] / 1000000
+    trade_fill_df["amount"] = trade_fill_df["amount"] / 1000000
+    conn.close()
+    return BotData(order_df, order_status_df, trade_fill_df)
+
+
+@st.cache_data(ttl=60)
+def get_ohlc(trading_pair: str, exchange: str, interval: str, start_timestamp: int, end_timestamp: int):
+    exchange = eval("ccxt." + exchange + "()")
+    limit = (end_timestamp - start_timestamp) / intervals[interval]
+    bars = exchange.fetch_ohlcv(trading_pair, timeframe=interval, since=start_timestamp * 1000, limit=int(limit))
+    df = pd.DataFrame(bars, columns=["timestamp", "open", "high", "low", "close", "volume"])
+    df["datetime"] = pd.to_datetime(df["timestamp"], unit="ms")
+    return df
+
+
+col11, col12 = st.columns(2)
+with col11:
+    db_names = [db_name for db_name in os.listdir("data") if db_name.endswith(".sqlite")]
+    selected_db_name = st.selectbox("Select a database to use:", db_names)
+    all_bots_data = get_data(selected_db_name)
+with col12:
+    selected_config_file = st.selectbox("Select a config file to analyze:",
+                                        all_bots_data.trade_fill["config_file_path"].unique())
+    if selected_config_file is not None:
+        strategy_data = all_bots_data.get_strategy_data(
+            selected_config_file)
+row = st.container()
+col11, col12, col13 = st.columns([1, 2, 3])
+with row:
+    with col11:
+        st.header(f"🏦 Market")
+        st.metric(label="Exchange", value=strategy_data.market.capitalize())
+        st.metric(label="Trading pair", value=strategy_data.symbol)
+    with col12:
+        st.header("📋 General stats")
+        col121, col122 = st.columns(2)
+        with col121:
+            st.metric(label='Start date', value=strategy_data.start_time.strftime("%Y-%m-%d %H:%M"))
+            st.metric(label='End date', value=strategy_data.end_time.strftime("%Y-%m-%d %H:%M"))
+            st.metric(label='Duration (Days)', value=round(strategy_data.duration_minutes / (60 * 24), 4))
+        with col122:
+            st.metric(label='Start Price', value=round(strategy_data.start_price, 4))
+            st.metric(label='End Price', value=round(strategy_data.end_price, 4))
+            st.metric(label='Price change', value=f"{round(strategy_data.price_change * 100, 2)} %")
+    with col13:
+        st.header("📈 Performance")
+        col131, col132, col133 = st.columns(3)
+        with col131:
+            st.metric(label='Total Trades', value=strategy_data.total_orders)
+            st.metric(label='Total Buy Trades', value=strategy_data.total_buy_trades)
+            st.metric(label='Total Sell Trades', value=strategy_data.total_sell_trades)
+        with col132:
+            st.metric(label='Inventory change in Base asset', value=round(strategy_data.inventory_change_base_asset, 4))
+            st.metric(label='Total Buy Trades Amount', value=strategy_data.total_buy_amount)
+            st.metric(label='Total Sell Trades Amount', value=strategy_data.total_sell_amount)
+        with col133:
+            st.metric(label='Trade PNL USD', value=round(strategy_data.trade_pnl_usd, 2))
+            st.metric(label='Average Buy Price', value=round(strategy_data.average_buy_price, 4))
+            st.metric(label='Average Sell Price', value=round(strategy_data.average_sell_price, 4))
+
+col41, col42, col43 = st.columns(3)
+
+exchange_name = strategy_data.orders["market"].unique().item().split("_")[0]
+trading_pair = strategy_data.orders["symbol"].unique().item().replace("-", "")
+
+c1, c2 = st.columns([1, 5])
+with c1:
+    interval = st.selectbox("Candles Interval:", intervals.keys(), index=3)
+
+date_array = pd.date_range(start=strategy_data.start_time, end=strategy_data.end_time, periods=60)
+ohlc_extra_time = 60 * 60 * 24
+with st.spinner("Loading candles..."):
+    candles_df = get_ohlc(trading_pair, exchange_name, interval, int(strategy_data.start_time.timestamp() - ohlc_extra_time),
+                          int(strategy_data.end_time.timestamp() + ohlc_extra_time))
+start_time, end_time = st.select_slider("Select a time range to analyze", options=date_array.tolist(), value=(date_array[0], date_array[-1]))
+candles_df_filtered = candles_df[(candles_df["timestamp"] >= int(start_time.timestamp() * 1000)) & (candles_df["timestamp"] <= int(end_time.timestamp() * 1000))]
+strategy_data_filtered = strategy_data.get_filtered_strategy_data(start_time, end_time)
+cg = CandlesGraph(candles_df_filtered, show_volume=True, extra_rows=2)
+cg.add_buy_trades(strategy_data_filtered.buys)
+cg.add_sell_trades(strategy_data_filtered.sells)
+cg.add_base_inventory_change(strategy_data_filtered)
+cg.add_trade_pnl(strategy_data_filtered)
+fig = cg.figure()
+st.plotly_chart(fig, use_container_width=True)
+
+st.subheader("💵Trades")
+st.write(strategy_data_filtered.trade_fill)
+
+st.subheader("📩 Orders")
+st.write(strategy_data_filtered.orders)
+
+st.subheader("⌕ Order Status")
+st.write(strategy_data_filtered.order_status)