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https://github.com/aljazceru/hummingbot-dashboard.git
synced 2026-01-26 00:24:20 +01:00
(feat) analyze page MVP
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drupman
@@ -1,113 +1,70 @@
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import constants
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from utils.st_utils import initialize_st_page
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from utils.optuna_database_manager import OptunaDBManager
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import pandas as pd
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import os
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import json
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import streamlit as st
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from quants_lab.strategy.strategy_analysis import StrategyAnalysis
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import plotly.graph_objs as go
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from quants_lab.strategy.strategy_analysis import StrategyAnalysis
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from utils.graphs import BacktestingGraphs
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from utils.optuna_database_manager import OptunaDBManager
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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="Analyze", icon="🔬", initial_sidebar_state="collapsed")
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def load_params(df: pd.DataFrame):
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trial_id_col = 'trial_id'
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param_name_col = 'param_name'
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param_value_col = 'param_value'
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distribution_json_col = 'distribution_json'
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nested_dict = {}
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for _, row in df.iterrows():
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trial_id = row[trial_id_col]
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param_name = row[param_name_col]
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param_value = row[param_value_col]
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distribution_json = row[distribution_json_col]
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if trial_id not in nested_dict:
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nested_dict[trial_id] = {}
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dist_json = json.loads(distribution_json)
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nested_dict[trial_id][param_name] = {
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'param_name': param_name,
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'param_value': param_value,
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'step': dist_json["attributes"]["step"],
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'low': dist_json["attributes"]["low"],
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'high': dist_json["attributes"]["high"],
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'log': dist_json["attributes"]["log"],
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}
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return nested_dict
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def load_studies(df: pd.DataFrame):
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study_id_col = 'study_id'
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trial_id_col = 'trial_id'
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nested_dict = {}
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for _, row in df.iterrows():
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study_id = row[study_id_col]
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trial_id = row[trial_id_col]
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data_dict = row.drop([study_id_col, trial_id_col]).to_dict()
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if study_id not in nested_dict:
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nested_dict[study_id] = {}
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nested_dict[study_id][trial_id] = data_dict
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return nested_dict
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@st.cache_resource
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def get_databases():
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sqlite_files = [db_name for db_name in os.listdir("data/backtesting") if db_name.endswith(".db")]
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databases_list = [OptunaDBManager(db) for db in sqlite_files]
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return {database.db_name: database for database in databases_list}
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def pnl_vs_maxdrawdown(df: pd.DataFrame):
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fig = go.Figure()
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fig.add_trace(go.Scatter(name="Pnl vs Max Drawdown",
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x=-100 * df["max_drawdown_pct"],
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y=100 * df["net_profit_pct"],
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mode="markers",
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text=None,
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hovertext=df["hover_text"]))
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fig.update_layout(
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title="PnL vs Max Drawdown",
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xaxis_title="Max Drawdown [%]",
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yaxis_title="Net Profit [%]",
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height=800
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)
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fig.data[0].text = []
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return fig
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databases_dict = {database.db_name: database for database in databases_list}
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return [x.db_name for x in databases_dict.values() if x.status == 'OK']
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def initialize_session_state_vars():
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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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if "backtesting_params" not in st.session_state:
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st.session_state.backtesting_params = {}
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initialize_session_state_vars()
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dbs = get_databases()
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db_names = [x.db_name for x in dbs.values() if x.status == 'OK']
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if not db_names:
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st.warning("No trades have been recorded in the selected database")
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if not dbs:
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st.warning("We couldn't find any Optuna database.")
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selected_db_name = None
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selected_db = None
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else:
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selected_db = st.selectbox("Select your database:", db_names)
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# Select database from selectbox
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selected_db = st.selectbox("Select your database:", dbs)
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# Instantiate database manager
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opt_db = OptunaDBManager(selected_db)
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st.plotly_chart(pnl_vs_maxdrawdown(opt_db.merged_df), use_container_width=True)
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strategies = load_directional_strategies(constants.DIRECTIONAL_STRATEGIES_PATH)
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studies = load_studies(opt_db.merged_df)
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# Load studies
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studies = opt_db.load_studies()
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# Choose study
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study_selected = st.selectbox("Select a study:", studies.keys())
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# Filter trials from selected study
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merged_df = opt_db.merged_df[opt_db.merged_df["study_name"] == study_selected]
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bt_graphs = BacktestingGraphs(merged_df)
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# Show and compare all of the study trials
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st.plotly_chart(bt_graphs.pnl_vs_maxdrawdown(), use_container_width=True)
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# Get study trials
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trials = studies[study_selected]
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# Choose trial
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trial_selected = st.selectbox("Select a trial to backtest", list(trials.keys()))
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trial = trials[trial_selected]
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# Transform trial config in a dictionary
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trial_config = json.loads(trial["config"])
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strategy = strategies[trial_config["name"]]
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strategy_config = strategy["config"]
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field_schema = strategy_config.schema()["properties"]
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# Strategy parameters section
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st.write("## Strategy parameters")
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# Load strategies (class, config, module)
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strategies = load_directional_strategies(constants.DIRECTIONAL_STRATEGIES_PATH)
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# Select strategy
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strategy = strategies[trial_config["name"]]
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# Get field schema
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field_schema = strategy["config"].schema()["properties"]
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c1, c2 = st.columns([5, 1])
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# Render every field according to schema
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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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@@ -138,36 +95,47 @@ else:
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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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st.subheader("Position config")
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position_configs = load_params(opt_db.trial_params)
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position_params = position_configs[trial_selected]
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# Backtesting parameters section
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st.write("## Backtesting parameters")
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# Get every trial params
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# TODO: Filter only from selected study
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backtesting_configs = opt_db.load_params()
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# Get trial backtesting params
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backtesting_params = backtesting_configs[trial_selected]
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col1, col2, col3 = st.columns(3)
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with col1:
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selected_order_amount = st.number_input("Order amount", value=50.0, min_value=0.1, max_value=999999999.99)
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selected_leverage = st.number_input("Leverage", value=10, min_value=1, max_value=200)
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selected_order_amount = st.number_input("Order amount",
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value=50.0,
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min_value=0.1,
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max_value=999999999.99)
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selected_leverage = st.number_input("Leverage",
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value=10,
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min_value=1,
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max_value=200)
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with col2:
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selected_initial_portfolio = st.number_input("Initial portfolio", value=10000.00, min_value=1.00,
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selected_initial_portfolio = st.number_input("Initial portfolio",
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value=10000.00,
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min_value=1.00,
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max_value=999999999.99)
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selected_time_limit = st.number_input("Time Limit",
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value=60 * 60 * position_params["time_limit"]["param_value"],
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min_value=60 * 60 * float(position_params["time_limit"]["low"]),
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max_value=60 * 60 * float(position_params["time_limit"]["high"]))
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value=60 * 60 * backtesting_params["time_limit"]["param_value"],
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min_value=60 * 60 * float(backtesting_params["time_limit"]["low"]),
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max_value=60 * 60 * float(backtesting_params["time_limit"]["high"]))
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with col3:
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selected_tp_multiplier = st.number_input("Take Profit Multiplier",
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value=position_params["take_profit_multiplier"]["param_value"],
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min_value=position_params["take_profit_multiplier"]["low"],
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max_value=position_params["take_profit_multiplier"]["high"])
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value=backtesting_params["take_profit_multiplier"]["param_value"],
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min_value=backtesting_params["take_profit_multiplier"]["low"],
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max_value=backtesting_params["take_profit_multiplier"]["high"])
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selected_sl_multiplier = st.number_input("Stop Loss Multiplier",
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value=position_params["stop_loss_multiplier"]["param_value"],
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min_value=position_params["stop_loss_multiplier"]["low"],
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max_value=position_params["stop_loss_multiplier"]["high"])
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run_backtesting_button = st.button("Run Backtesting!")
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if run_backtesting_button:
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value=backtesting_params["stop_loss_multiplier"]["param_value"],
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min_value=backtesting_params["stop_loss_multiplier"]["low"],
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max_value=backtesting_params["stop_loss_multiplier"]["high"])
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if st.button("Run Backtesting!"):
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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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try:
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market_data, positions = strategy.run_backtesting(
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start='2021-04-01',
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order_amount=selected_order_amount,
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leverage=selected_order_amount,
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initial_portfolio=selected_initial_portfolio,
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@@ -180,66 +148,9 @@ else:
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positions=positions,
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candles_df=market_data,
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)
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col1, col2 = st.columns(2)
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with col1:
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st.subheader("🏦 Market")
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with col2:
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st.subheader("📋 General stats")
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Exchange", st.session_state["strategy_params"]["exchange"])
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with col2:
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st.metric("Trading Pair", st.session_state["strategy_params"]["trading_pair"])
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with col3:
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st.metric("Start date", strategy_analysis.start_date().strftime("%Y-%m-%d %H:%M"))
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st.metric("End date", strategy_analysis.end_date().strftime("%Y-%m-%d %H:%M"))
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with col4:
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st.metric("Duration (hours)", f"{strategy_analysis.duration_in_minutes() / 60:.2f}")
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st.metric("Price change", st.session_state["strategy_params"]["trading_pair"])
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st.subheader("📈 Performance")
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col1, col2, col3, col4, col5, col6, col7, col8 = st.columns(8)
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with col1:
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st.metric("Net PnL USD",
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f"{strategy_analysis.net_profit_usd():.2f}",
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delta=f"{100 * strategy_analysis.net_profit_pct():.2f}%",
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help="The overall profit or loss achieved.")
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with col2:
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st.metric("Total positions",
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f"{strategy_analysis.total_positions()}",
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help="The total number of closed trades, winning and losing.")
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with col3:
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st.metric("% Profitable",
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f"{(len(strategy_analysis.win_signals()) / strategy_analysis.total_positions()):.2f}",
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help="The percentage of winning trades, the number of winning trades divided by the"
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" total number of closed trades")
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with col4:
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st.metric("Profit factor",
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f"{strategy_analysis.profit_factor():.2f}",
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help="The amount of money the strategy made for every unit of money it lost, "
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"gross profits divided by gross losses.")
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with col5:
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st.metric("Max Drawdown",
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f"{strategy_analysis.max_drawdown_usd():.2f}",
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delta=f"{strategy_analysis.max_drawdown_pct():.2f}%",
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help="The greatest loss drawdown, i.e., the greatest possible loss the strategy had compared "
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"to its highest profits")
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with col6:
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st.metric("Avg Profit",
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f"{strategy_analysis.avg_profit():.2f}",
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help="The sum of money gained or lost by the average trade, Net Profit divided by "
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"the overall number of closed trades.")
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with col7:
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st.metric("Avg Minutes",
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f"{strategy_analysis.avg_trading_time_in_minutes():.2f}",
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help="The average number of minutes that elapsed during trades for all closed trades.")
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with col8:
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st.metric("Sharpe Ratio",
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f"{strategy_analysis.sharpe_ratio():.2f}",
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help="The Sharpe ratio is a measure that quantifies the risk-adjusted return of an investment"
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" or portfolio. It compares the excess return earned above a risk-free rate per unit of"
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" risk taken.")
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st.plotly_chart(strategy_analysis.pnl_over_time(), use_container_width=True)
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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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metrics_container = bt_graphs.get_trial_metrics(strategy_analysis,
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add_positions=add_positions,
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add_volume=add_volume,
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add_pnl=add_pnl)
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except FileNotFoundError:
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st.warning(f"The requested candles could not be found.")
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