mirror of
https://github.com/aljazceru/hummingbot-dashboard.git
synced 2025-12-28 18:54:22 +01:00
91 lines
3.8 KiB
Python
91 lines
3.8 KiB
Python
import time
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import webbrowser
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from types import SimpleNamespace
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import streamlit as st
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from streamlit_elements import elements, mui
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import constants
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from quants_lab.strategy.strategy_analysis import StrategyAnalysis
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from ui_components.dashboard import Dashboard
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from ui_components.directional_strategies_file_explorer import DirectionalStrategiesFileExplorer
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from ui_components.directional_strategy_creation_card import DirectionalStrategyCreationCard
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from ui_components.editor import Editor
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from ui_components.optimization_creation_card import OptimizationCreationCard
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from ui_components.optimization_run_card import OptimizationRunCard
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from ui_components.optimizations_file_explorer import OptimizationsStrategiesFileExplorer
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from utils import os_utils
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from utils.os_utils import load_directional_strategies
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from utils.st_utils import initialize_st_page
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initialize_st_page(title="Simulate", icon="📈", initial_sidebar_state="collapsed")
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# Start content here
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if "strategy_params" not in st.session_state:
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st.session_state.strategy_params = {}
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# TODO:
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# * Add videos explaining how to the triple barrier method works and how the backtesting is designed,
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# link to video of how to create a strategy, etc in a toggle.
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# * Add performance analysis graphs of the backtesting run
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strategies = load_directional_strategies(constants.DIRECTIONAL_STRATEGIES_PATH)
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strategy_to_optimize = st.selectbox("Select strategy to backtest", strategies.keys())
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strategy = strategies[strategy_to_optimize]
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strategy_config = strategy["config"]
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field_schema = strategy_config.schema()["properties"]
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st.write("## Strategy parameters")
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c1, c2 = st.columns([5, 1])
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with c1:
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columns = st.columns(4)
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column_index = 0
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for field_name, properties in field_schema.items():
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field_type = properties["type"]
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with columns[column_index]:
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if field_type in ["number", "integer"]:
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field_value = st.number_input(field_name,
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value=properties["default"],
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min_value=properties.get("minimum"),
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max_value=properties.get("maximum"),
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key=field_name)
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elif field_type == "string":
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field_value = st.text_input(field_name, value=properties["default"])
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elif field_type == "boolean":
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# TODO: Add support for boolean fields in optimize tab
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field_value = st.checkbox(field_name, value=properties["default"])
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else:
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raise ValueError(f"Field type {field_type} not supported")
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st.session_state["strategy_params"][field_name] = field_value
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column_index = (column_index + 1) % 4
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with c2:
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add_positions = st.checkbox("Add positions", value=True)
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add_volume = st.checkbox("Add volume", value=True)
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add_pnl = st.checkbox("Add PnL", value=True)
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run_backtesting_button = st.button("Run Backtesting!")
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if run_backtesting_button:
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config = strategy["config"](**st.session_state["strategy_params"])
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strategy = strategy["class"](config=config)
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# TODO: add form for order amount, leverage, tp, sl, etc.
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market_data, positions = strategy.run_backtesting(
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start='2021-04-01',
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order_amount=50,
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leverage=20,
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initial_portfolio=100,
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take_profit_multiplier=2.3,
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stop_loss_multiplier=1.2,
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time_limit=60 * 60 * 3,
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std_span=None,
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)
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strategy_analysis = StrategyAnalysis(
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positions=positions,
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candles_df=market_data,
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)
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st.text(strategy_analysis.text_report())
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# TODO: check why the pnl is not being plotted
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strategy_analysis.create_base_figure(volume=add_volume, positions=add_positions, trade_pnl=add_pnl)
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st.plotly_chart(strategy_analysis.figure(), use_container_width=True)
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