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https://github.com/aljazceru/hummingbot-dashboard.git
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228 lines
13 KiB
Python
228 lines
13 KiB
Python
from hummingbot.core.data_type.common import PositionMode, TradeType, OrderType
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from hummingbot.data_feed.candles_feed.candles_factory import CandlesConfig
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from hummingbot.smart_components.strategy_frameworks.data_types import OrderLevel, TripleBarrierConf
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from hummingbot.smart_components.strategy_frameworks.directional_trading import DirectionalTradingBacktestingEngine
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from hummingbot.smart_components.utils import ConfigEncoderDecoder
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import constants
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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 decimal import Decimal
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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_controllers
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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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@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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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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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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# 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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# 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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filters_column, scatter_column = st.columns([1, 6])
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with filters_column:
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accuracy = st.slider("Accuracy", min_value=0.0, max_value=1.0, value=[0.4, 1.0], step=0.01)
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net_profit = st.slider("Net PNL (%)", min_value=merged_df["net_pnl_pct"].min(), max_value=merged_df["net_pnl_pct"].max(),
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value=[merged_df["net_pnl_pct"].min(), merged_df["net_pnl_pct"].max()], step=0.01)
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max_drawdown = st.slider("Max Drawdown (%)", min_value=merged_df["max_drawdown_pct"].min(), max_value=merged_df["max_drawdown_pct"].max(),
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value=[merged_df["max_drawdown_pct"].min(), merged_df["max_drawdown_pct"].max()], step=0.01)
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total_positions = st.slider("Total Positions", min_value=merged_df["total_positions"].min(), max_value=merged_df["total_positions"].max(),
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value=[merged_df["total_positions"].min(), merged_df["total_positions"].max()], step=1)
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net_profit_filter = (merged_df["net_pnl_pct"] >= net_profit[0]) & (merged_df["net_pnl_pct"] <= net_profit[1])
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accuracy_filter = (merged_df["accuracy"] >= accuracy[0]) & (merged_df["accuracy"] <= accuracy[1])
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max_drawdown_filter = (merged_df["max_drawdown_pct"] >= max_drawdown[0]) & (merged_df["max_drawdown_pct"] <= max_drawdown[1])
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total_positions_filter = (merged_df["total_positions"] >= total_positions[0]) & (merged_df["total_positions"] <= total_positions[1])
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with scatter_column:
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bt_graphs = BacktestingGraphs(merged_df[net_profit_filter & accuracy_filter & max_drawdown_filter & total_positions_filter])
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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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encoder_decoder = ConfigEncoderDecoder(TradeType, OrderType, PositionMode)
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trial_config = encoder_decoder.decode(json.loads(trial["config"]))
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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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controllers = load_controllers(constants.CONTROLLERS_PATH)
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# Select strategy
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controller = controllers[trial_config["strategy_name"]]
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# Get field schema
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field_schema = controller["config"].schema()["properties"]
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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.get("type", "string")
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field_value = trial_config[field_name]
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if field_name not in ["candles_config", "order_levels", "position_mode"]:
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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=field_value,
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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=field_value)
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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=field_value)
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else:
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raise ValueError(f"Field type {field_type} not supported")
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else:
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if field_name == "candles_config":
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st.write("---")
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st.write(f"## Candles Config:")
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candles = []
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for i, candles_config in enumerate(field_value):
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st.write(f"#### Candle {i}:")
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c11, c12, c13, c14 = st.columns(4)
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with c11:
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connector = st.text_input("Connector", value=candles_config["connector"])
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with c12:
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trading_pair = st.text_input("Trading pair", value=candles_config["trading_pair"])
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with c13:
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interval = st.text_input("Interval", value=candles_config["interval"])
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with c14:
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max_records = st.number_input("Max records", value=candles_config["max_records"])
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st.write("---")
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candles.append(CandlesConfig(connector=connector, trading_pair=trading_pair, interval=interval,
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max_records=max_records))
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field_value = candles
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elif field_name == "order_levels":
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new_levels = []
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st.write(f"## Order Levels:")
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for order_level in field_value:
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st.write(f"### Level {order_level['level']} {order_level['side'].name}")
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ol_c1, ol_c2 = st.columns([5, 1])
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with ol_c1:
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st.write("#### Triple Barrier config:")
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c21, c22, c23, c24, c25 = st.columns(5)
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triple_barrier_conf_level = order_level["triple_barrier_conf"]
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with c21:
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take_profit = st.number_input("Take profit", value=float(triple_barrier_conf_level["take_profit"]),
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key=f"{order_level['level']}_{order_level['side'].name}_tp")
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with c22:
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stop_loss = st.number_input("Stop Loss", value=float(triple_barrier_conf_level["stop_loss"]),
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key=f"{order_level['level']}_{order_level['side'].name}_sl")
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with c23:
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time_limit = st.number_input("Time Limit", value=triple_barrier_conf_level["time_limit"],
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key=f"{order_level['level']}_{order_level['side'].name}_tl")
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with c24:
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ts_ap = st.number_input("Trailing Stop Activation Price", value=float(triple_barrier_conf_level["trailing_stop_activation_price_delta"]),
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key=f"{order_level['level']}_{order_level['side'].name}_tsap", format="%.4f")
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with c25:
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ts_td = st.number_input("Trailing Stop Trailing Delta", value=float(triple_barrier_conf_level["trailing_stop_trailing_delta"]),
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key=f"{order_level['level']}_{order_level['side'].name}_tstd", format="%.4f")
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with ol_c2:
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st.write("#### Position config:")
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c31, c32 = st.columns(2)
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with c31:
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order_amount = st.number_input("Order amount USD", value=float(order_level["order_amount_usd"]),
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key=f"{order_level['level']}_{order_level['side'].name}_oa")
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with c32:
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cooldown_time = st.number_input("Cooldown time", value=order_level["cooldown_time"],
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key=f"{order_level['level']}_{order_level['side'].name}_cd")
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triple_barrier_conf = TripleBarrierConf(stop_loss=Decimal(stop_loss), take_profit=Decimal(take_profit),
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time_limit=time_limit,
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trailing_stop_activation_price_delta=Decimal(ts_ap),
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trailing_stop_trailing_delta=Decimal(ts_td),
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open_order_type=OrderType.MARKET)
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new_levels.append(OrderLevel(level=order_level["level"], side=order_level["side"],
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order_amount_usd=order_amount, cooldown_time=cooldown_time,
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triple_barrier_conf=triple_barrier_conf))
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st.write("---")
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field_value = new_levels
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elif field_name == "position_mode":
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field_value = PositionMode.HEDGE
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else:
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field_value = None
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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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st.write("### Backtesting period")
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col1, col2, col3, col4 = st.columns([1, 1, 1, 0.5])
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with col1:
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trade_cost = st.number_input("Trade cost",
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value=0.0006,
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min_value=0.0001, format="%.4f", )
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with col2:
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initial_portfolio_usd = st.number_input("Initial portfolio usd",
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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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with col3:
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start = st.text_input("Start", value="2023-01-01")
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end = st.text_input("End", value="2023-08-01")
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c1, c2 = st.columns([1, 1])
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with col4:
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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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save_config = st.button("💾Save controller config!")
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config = controller["config"](**st.session_state["strategy_params"])
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controller = controller["class"](config=config)
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if save_config:
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encoder_decoder = ConfigEncoderDecoder(TradeType, OrderType, PositionMode)
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encoder_decoder.yaml_dump(config.dict(),
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f"hummingbot_files/controller_configs/{config.strategy_name}_{trial_selected}.yml")
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run_backtesting_button = st.button("⚙️Run Backtesting!")
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if run_backtesting_button:
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try:
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engine = DirectionalTradingBacktestingEngine(controller=controller)
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engine.load_controller_data("./data/candles")
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backtesting_results = engine.run_backtesting(initial_portfolio_usd=initial_portfolio_usd,
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trade_cost=trade_cost,
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start=start, end=end)
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strategy_analysis = StrategyAnalysis(
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positions=backtesting_results["executors_df"],
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candles_df=backtesting_results["processed_data"],
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)
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metrics_container = BacktestingGraphs(backtesting_results["processed_data"]).get_trial_metrics(
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strategy_analysis,
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add_positions=add_positions,
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add_volume=add_volume)
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except FileNotFoundError:
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st.warning(f"The requested candles could not be found.")
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