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Merge pull request #15 from hummingbot/feat/database_manager
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Feat/database manager
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@nikspz
nikspz authored May 11, 2023
2 parents 17f0a3a + 0b8648f commit 14162c2
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Showing 6 changed files with 375 additions and 180 deletions.
2 changes: 1 addition & 1 deletion .streamlit/config.toml
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[theme]
base="dark"
base="light"
font="monospace"
[server]
port=8501
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2 changes: 1 addition & 1 deletion environment_conda.yml
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name: streamlit-apps
name: dashboard
channels:
- conda-forge
dependencies:
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203 changes: 102 additions & 101 deletions pages/2_🚀_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.database_manager import DatabaseManager
from utils.graphs import CandlesGraph

st.set_page_config(
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st.title("🚀 Strategy Performance")

intervals = {
"1m": 60,
# "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)
def get_database(db_name: str):
db_manager = DatabaseManager(db_name)
return db_manager


@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))
connector = getattr(ccxt, exchange)()
limit = max(int((end_timestamp - start_timestamp) / intervals[interval]), 10)
bars = connector.fetch_ohlcv(trading_pair.replace("-", ""), timeframe=interval, since=start_timestamp * 1000, limit=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)

exchange_name = strategy_data.market
trading_pair = strategy_data.symbol.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
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)

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_filtered.market.capitalize())
st.metric(label="Trading pair", value=strategy_data_filtered.symbol)
with col12:
st.header("📋 General stats")
col121, col122 = st.columns(2)
with col121:
st.metric(label='Start date', value=strategy_data_filtered.start_time.strftime("%Y-%m-%d %H:%M"))
st.metric(label='End date', value=strategy_data_filtered.end_time.strftime("%Y-%m-%d %H:%M"))
st.metric(label='Duration (Hours)', value=round(strategy_data_filtered.duration_seconds / 3600, 2))
with col122:
st.metric(label='Start Price', value=round(strategy_data_filtered.start_price, 4))
st.metric(label='End Price', value=round(strategy_data_filtered.end_price, 4))
st.metric(label='Price change', value=f"{round(strategy_data_filtered.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_filtered.total_orders)
st.metric(label='Total Buy Trades', value=strategy_data_filtered.total_buy_trades)
st.metric(label='Total Sell Trades', value=strategy_data_filtered.total_sell_trades)
with col132:
st.metric(label='Inventory change in Base asset', value=round(strategy_data_filtered.inventory_change_base_asset, 4))
st.metric(label='Total Buy Trades Amount', value=strategy_data_filtered.total_buy_amount)
st.metric(label='Total Sell Trades Amount', value=strategy_data_filtered.total_sell_amount)
with col133:
st.metric(label='Trade PNL USD', value=round(strategy_data_filtered.trade_pnl_usd, 2))
st.metric(label='Average Buy Price', value=round(strategy_data_filtered.average_buy_price, 4))
st.metric(label='Average Sell Price', value=round(strategy_data_filtered.average_sell_price, 4))

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)
with st.container():
col1, col2 = st.columns(2)
with col1:
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 if len(db_names) > 0 else ["No databases found"])
if selected_db_name == "No databases found":
st.warning("No databases available to analyze. Please run a backtesting first.")
else:
db_manager = get_database(selected_db_name)
with col2:
selected_config_file = st.selectbox("Select a config file to analyze:", db_manager.get_config_files())
if selected_config_file is not None:
exchanges_trading_pairs = db_manager.get_exchanges_trading_pairs_by_config_file(selected_config_file)
strategy_data = db_manager.get_strategy_data(selected_config_file)

with st.container():
col1, col2, col3 = st.columns(3)
with col1:
selected_exchange = st.selectbox("Select an exchange:", list(exchanges_trading_pairs.keys()))
with col2:
selected_trading_pair = st.selectbox("Select a trading pair:", exchanges_trading_pairs[selected_exchange])
with col3:
interval = st.selectbox("Candles Interval:", intervals.keys(), index=0)

if selected_exchange and selected_trading_pair:
single_market_strategy_data = strategy_data.get_single_market_strategy_data(selected_exchange, selected_trading_pair)
date_array = pd.date_range(start=strategy_data.start_time, end=strategy_data.end_time, periods=60)
ohlc_extra_time = 60
with st.spinner("Loading candles..."):
candles_df = get_ohlc(single_market_strategy_data.trading_pair, single_market_strategy_data.exchange, 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 = single_market_strategy_data.get_filtered_strategy_data(start_time, end_time)

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_filtered.exchange.capitalize())
st.metric(label="Trading pair", value=strategy_data_filtered.trading_pair.upper())
with col12:
st.header("📋 General stats")
col121, col122 = st.columns(2)
with col121:
st.metric(label='Duration (Hours)', value=round(strategy_data_filtered.duration_seconds / 3600, 2))
st.metric(label='Start date', value=strategy_data_filtered.start_time.strftime("%Y-%m-%d %H:%M"))
st.metric(label='End date', value=strategy_data_filtered.end_time.strftime("%Y-%m-%d %H:%M"))
with col122:
st.metric(label='Price change', value=f"{round(strategy_data_filtered.price_change * 100, 2)} %")
with col13:
st.header("📈 Performance")
col131, col132, col133, col134 = st.columns(4)
with col131:
st.metric(label=f'Net PNL {strategy_data_filtered.quote_asset}', value=round(strategy_data_filtered.net_pnl_quote, 2))
st.metric(label=f'Trade PNL {strategy_data_filtered.quote_asset}', value=round(strategy_data_filtered.trade_pnl_quote, 2))
st.metric(label=f'Fees {strategy_data_filtered.quote_asset}', value=round(strategy_data_filtered.cum_fees_in_quote, 2))
with col132:
st.metric(label='Total Trades', value=strategy_data_filtered.total_orders)
st.metric(label='Total Buy Trades', value=strategy_data_filtered.total_buy_trades)
st.metric(label='Total Sell Trades', value=strategy_data_filtered.total_sell_trades)
with col133:
st.metric(label='Inventory change in Base asset',
value=round(strategy_data_filtered.inventory_change_base_asset, 4))
st.metric(label='Total Buy Trades Amount', value=round(strategy_data_filtered.total_buy_amount, 2))
st.metric(label='Total Sell Trades Amount', value=round(strategy_data_filtered.total_sell_amount, 2))
with col134:
st.metric(label='End Price', value=round(strategy_data_filtered.end_price, 4))
st.metric(label='Average Buy Price', value=round(strategy_data_filtered.average_buy_price, 4))
st.metric(label='Average Sell Price', value=round(strategy_data_filtered.average_sell_price, 4))

cg = CandlesGraph(candles_df_filtered, show_volume=True, extra_rows=4)
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_net_pnl(strategy_data_filtered)
cg.add_trade_pnl(strategy_data_filtered)
cg.add_trade_fee(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)
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