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Feat/database manager #15

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merged 10 commits into from
May 11, 2023
Merged

Feat/database manager #15

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2a255f8
(feat) change environment name
cardosofede May 3, 2023
a38b093
(feat) clean up Strategy data class and add SingleMarketStrategyData
cardosofede May 3, 2023
3f4c30f
(feat) add database_manager.py
cardosofede May 3, 2023
a7f6a4f
(feat) adapt code to use database manager
cardosofede May 3, 2023
1336798
(feat) update theme since ohlc graph is better in ligth
cardosofede May 4, 2023
38f373f
(feat) create database manage class
cardosofede May 4, 2023
344ea2f
(feat) added fee cost graphs
cardosofede May 4, 2023
daa8089
(feat) add some properties and refactored single market strategy class
cardosofede May 4, 2023
a4e20d0
(feat) included improvements in graphs, metrics and database manager …
cardosofede May 4, 2023
0b8648f
(fix) check if database exists before getting the data
cardosofede May 9, 2023
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2 changes: 1 addition & 1 deletion .streamlit/config.toml
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
[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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@@ -1,4 +1,4 @@
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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@@ -1,14 +1,12 @@
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(
Expand All @@ -20,118 +18,121 @@
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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