mirror of
https://github.com/aljazceru/hummingbot-dashboard.git
synced 2026-01-11 01:14:25 +01:00
(feat) initialize controller configs pages
This commit is contained in:
cardosofede
@@ -1,13 +1,15 @@
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from datetime import datetime
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import streamlit as st
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import pandas as pd
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import plotly.graph_objects as go
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import pandas_ta as ta
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import yaml
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import pandas_ta as ta # noqa: F401
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from hummingbot.connector.connector_base import OrderType
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from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
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from utils.backend_api_client import BackendAPIClient
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from utils.st_utils import initialize_st_page
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from backend.services.backend_api_client import BackendAPIClient
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from frontend.st_utils import initialize_st_page
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# Initialize the Streamlit page
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initialize_st_page(title="Bollinger V1", icon="📈", initial_sidebar_state="expanded")
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@@ -207,6 +209,7 @@ config = {
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"bb_short_threshold": bb_short_threshold
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}
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yaml_config = yaml.dump(config, default_flow_style=False)
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with c3:
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@@ -222,4 +225,29 @@ with c3:
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if upload_config_to_backend:
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backend_api_client = BackendAPIClient.get_instance(host=BACKEND_API_HOST, port=BACKEND_API_PORT)
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backend_api_client.add_controller_config(config)
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st.success("Config uploaded successfully!")
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st.success("Config uploaded successfully!")
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st.write("---")
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st.write("### Backtesting")
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c1, c2, c3, c4, c5 = st.columns(5)
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with c1:
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start_datetime = st.date_input("Start Date", datetime(2024, 5, 1))
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with c2:
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end_datetime = st.date_input("End Date", datetime(2024, 5, 1))
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with c3:
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backtesting_resolution = st.selectbox("Backtesting Resolution", options=["1m", "3m", "5m", "15m", "30m"], index=1)
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with c4:
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trade_cost = st.number_input("Trade Cost", min_value=0.0, value=0.0006, step=0.0001)
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with c5:
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run_backtesting = st.button("Run Backtesting")
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if run_backtesting:
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backend_api_client = BackendAPIClient.get_instance(host=BACKEND_API_HOST, port=BACKEND_API_PORT)
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backtesting_results = backend_api_client.run_backtesting(
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start_time=int(start_datetime.timestamp()) * 1000,
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end_time=(end_datetime.timestamp()) * 1000,
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backtesting_resolution=backtesting_resolution,
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trade_cost=trade_cost,
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config=yaml_config,
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)
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st.write(backtesting_results)
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@@ -5,9 +5,9 @@ from decimal import Decimal
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import yaml
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from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
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from utils.backend_api_client import BackendAPIClient
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from utils.st_utils import initialize_st_page
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from backend.services.backend_api_client import BackendAPIClient
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from frontend.components.st_inputs import normalize, distribution_inputs, get_distribution
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from frontend.st_utils import initialize_st_page
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# Initialize the Streamlit page
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initialize_st_page(title="D-Man Maker V2", icon="🧙♂️", initial_sidebar_state="collapsed")
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@@ -1,14 +1,12 @@
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import streamlit as st
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import pandas as pd
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import pandas_ta as ta
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import plotly.graph_objects as go
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import yaml
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from hummingbot.connector.connector_base import OrderType
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from plotly.subplots import make_subplots
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from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
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from utils.backend_api_client import BackendAPIClient
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from utils.st_utils import initialize_st_page
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from backend.services.backend_api_client import BackendAPIClient
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from frontend.st_utils import initialize_st_page
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# Initialize the Streamlit page
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initialize_st_page(title="D-Man V5", icon="📊", initial_sidebar_state="expanded")
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@@ -1,14 +1,13 @@
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import streamlit as st
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import pandas as pd
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import plotly.graph_objects as go
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import pandas_ta as ta
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import yaml
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from hummingbot.connector.connector_base import OrderType
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from pykalman import KalmanFilter
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from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
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from utils.backend_api_client import BackendAPIClient
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from utils.st_utils import initialize_st_page
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from backend.services.backend_api_client import BackendAPIClient
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from frontend.st_utils import initialize_st_page
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# Initialize the Streamlit page
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initialize_st_page(title="Kalman Filter V1", icon="📈", initial_sidebar_state="expanded")
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@@ -1,14 +1,12 @@
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import streamlit as st
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import pandas as pd
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import pandas_ta as ta
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import plotly.graph_objects as go
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import yaml
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from hummingbot.connector.connector_base import OrderType
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from plotly.subplots import make_subplots
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from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
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from utils.backend_api_client import BackendAPIClient
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from utils.st_utils import initialize_st_page
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from backend.services.backend_api_client import BackendAPIClient
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from frontend.st_utils import initialize_st_page
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# Initialize the Streamlit page
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initialize_st_page(title="MACD_BB V1", icon="📊", initial_sidebar_state="expanded")
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@@ -1,239 +1,35 @@
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import pandas as pd
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import streamlit as st
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from hummingbot.connector.connector_base import OrderType
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from plotly.subplots import make_subplots
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import plotly.graph_objects as go
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from decimal import Decimal
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import yaml
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from frontend.components.st_inputs import initialize_st_page
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from backend.services.backend_api_client import BackendAPIClient
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from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
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from utils.backend_api_client import BackendAPIClient
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from utils.st_utils import initialize_st_page
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from frontend.components.st_inputs import normalize, distribution_inputs, get_distribution
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# Import submodules
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from frontend.pages.pmm_simple.user_inputs import user_inputs
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from frontend.pages.pmm_simple.order_calculation import calculate_orders
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from frontend.data_viz.visualization import visualize_orders
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from frontend.pages.pmm_simple.config_handling import handle_config
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from frontend.components.backtesting import backtesting_section
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# Initialize the Streamlit page
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initialize_st_page(title="PMM Simple", icon="👨🏫", initial_sidebar_state="collapsed")
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backend_api_client = BackendAPIClient.get_instance(host=BACKEND_API_HOST, port=BACKEND_API_PORT)
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# Page content
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st.text("This tool will let you create a config for PMM Simple and upload it to the BackendAPI.")
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st.write("---")
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c1, c2, c3, c4, c5, c6, c7 = st.columns(7)
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# Get user inputs
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inputs = user_inputs()
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with c1:
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connector = st.text_input("Connector", value="binance_perpetual")
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with c2:
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trading_pair = st.text_input("Trading Pair", value="WLD-USDT")
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with c3:
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total_amount_quote = st.number_input("Total amount of quote", value=1000)
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with c4:
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leverage = st.number_input("Leverage", value=20)
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position_mode = st.selectbox("Position Mode", ("HEDGE", "ONEWAY"), index=0)
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with c5:
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executor_refresh_time = st.number_input("Refresh Time (minutes)", value=3)
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cooldown_time = st.number_input("Cooldown Time (minutes)", value=3)
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with c6:
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sl = st.number_input("Stop Loss (%)", min_value=0.0, max_value=100.0, value=2.0, step=0.1)
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tp = st.number_input("Take Profit (%)", min_value=0.0, max_value=100.0, value=3.0, step=0.1)
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take_profit_order_type = st.selectbox("Take Profit Order Type", (OrderType.LIMIT, OrderType.MARKET))
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with c7:
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ts_ap = st.number_input("Trailing Stop Activation Price (%)", min_value=0.0, max_value=100.0, value=1.0, step=0.1)
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ts_delta = st.number_input("Trailing Stop Delta (%)", min_value=0.0, max_value=100.0, value=0.3, step=0.1)
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time_limit = st.number_input("Time Limit (minutes)", min_value=0, value=60 * 6)
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# Calculate orders based on inputs
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order_data = calculate_orders(inputs)
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# Visualize orders
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visualize_orders(order_data)
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# Handle configuration
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handle_config(inputs, order_data, backend_api_client)
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# Executors configuration
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col_buy, col_sell = st.columns(2)
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with col_buy:
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st.header("Buy Order Settings")
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buy_order_levels = st.number_input("Number of Buy Order Levels", min_value=1, value=2)
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with col_sell:
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st.header("Sell Order Settings")
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sell_order_levels = st.number_input("Number of Sell Order Levels", min_value=1, value=2)
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col_buy_spreads, col_buy_amounts, col_sell_spreads, col_sell_amounts = st.columns(4)
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# Inputs for buy orders
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with col_buy_spreads:
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buy_spread_dist_type, buy_spread_start, buy_spread_base, buy_spread_scaling, buy_spread_step, buy_spread_ratio, buy_manual_spreads = distribution_inputs(
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col_buy_spreads, "Spread", buy_order_levels)
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with col_buy_amounts:
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buy_amount_dist_type, buy_amount_start, buy_amount_base, buy_amount_scaling, buy_amount_step, buy_amount_ratio, buy_manual_amounts = distribution_inputs(
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col_buy_amounts, "Amount", buy_order_levels)
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with col_sell_spreads:
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sell_spread_dist_type, sell_spread_start, sell_spread_base, sell_spread_scaling, sell_spread_step, sell_spread_ratio, sell_manual_spreads = distribution_inputs(
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col_sell_spreads, "Spread", sell_order_levels)
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with col_sell_amounts:
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sell_amount_dist_type, sell_amount_start, sell_amount_base, sell_amount_scaling, sell_amount_step, sell_amount_ratio, sell_manual_amounts = distribution_inputs(
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col_sell_amounts, "Amount", sell_order_levels)
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buy_spread_distributions = get_distribution(buy_spread_dist_type, buy_order_levels, buy_spread_start, buy_spread_base,
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buy_spread_scaling, buy_spread_step, buy_spread_ratio, buy_manual_spreads)
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sell_spread_distributions = get_distribution(sell_spread_dist_type, sell_order_levels, sell_spread_start,
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sell_spread_base, sell_spread_scaling, sell_spread_step, sell_spread_ratio,
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sell_manual_spreads)
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buy_amount_distributions = normalize(
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get_distribution(buy_amount_dist_type, buy_order_levels, buy_amount_start, buy_amount_base, buy_amount_scaling,
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buy_amount_step, buy_amount_ratio, buy_manual_amounts))
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sell_amount_distributions = normalize(
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get_distribution(sell_amount_dist_type, sell_order_levels, sell_amount_start, sell_amount_base, sell_amount_scaling,
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sell_amount_step, sell_amount_ratio, sell_manual_amounts))
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all_orders_amount_normalized = normalize(buy_amount_distributions + sell_amount_distributions)
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buy_order_amounts_quote = [amount * total_amount_quote for amount in
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all_orders_amount_normalized[:buy_order_levels]]
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sell_order_amounts_quote = [amount * total_amount_quote for amount in
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all_orders_amount_normalized[buy_order_levels:]]
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# Initialize your figure with a dark theme
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fig = make_subplots(specs=[[{"secondary_y": True}]])
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fig.update_layout(
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template="plotly_dark",
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plot_bgcolor='rgba(0, 0, 0, 0)', # Transparent background
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paper_bgcolor='rgba(0, 0, 0, 0.1)', # Lighter shade for the paper
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title="Market Maker Order Distribution",
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xaxis_title="Spread (%)",
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yaxis_title="Amount (Quote)",
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legend_title="Order Type",
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font=dict(color='white', size=12) # Consistent font color and size
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)
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# Define colors for buy and sell orders
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colors = {
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'buy': '#32CD32', # Green for buy orders
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'sell': '#FF6347' # Tomato red for sell orders
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}
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# Add traces for buy and sell orders
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# Buy orders on the negative side of x-axis
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fig.add_trace(go.Bar(
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x=[-dist for dist in buy_spread_distributions],
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y=buy_order_amounts_quote,
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name='Buy Orders',
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marker_color=colors['buy'],
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width=[0.2] * buy_order_levels # Adjust the width of the bars as needed
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), secondary_y=False)
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# Sell orders on the positive side of x-axis
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fig.add_trace(go.Bar(
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x=sell_spread_distributions,
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y=sell_order_amounts_quote,
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name='Sell Orders',
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marker_color=colors['sell'],
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width=[0.2] * buy_order_levels # Adjust the width of the bars as needed
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), secondary_y=False)
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# Annotations can be added for each bar to display the value on top
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for i, value in enumerate(buy_order_amounts_quote):
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fig.add_annotation(
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x=-buy_spread_distributions[i],
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y=value + 10, # Offset the text slightly above the bar
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text=str(round(value, 2)),
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showarrow=False,
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font=dict(color=colors['buy'], size=10)
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)
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for i, value in enumerate(sell_order_amounts_quote):
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fig.add_annotation(
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x=sell_spread_distributions[i],
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y=value + 10, # Offset the text slightly above the bar
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text=str(round(value, 2)),
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showarrow=False,
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font=dict(color=colors['sell'], size=10)
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)
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# Optional: Add horizontal line or extra annotations if needed
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# e.g., for average, threshold, or specific markers
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# Update the layout to make it responsive and visually appealing
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fig.update_layout(
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height=600,
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width=800,
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margin=dict(l=20, r=20, t=50, b=20)
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)
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# Display the figure in Streamlit
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st.plotly_chart(fig, use_container_width=True)
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# Create DataFrame for Buy Orders
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buy_orders_df = pd.DataFrame({
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"Level": range(1, buy_order_levels + 1),
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"Spread (%)": [-dist for dist in buy_spread_distributions],
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"Amount (Quote)": buy_order_amounts_quote,
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"Take Profit ($)": [float(amount) * (tp / 100) for amount in buy_order_amounts_quote],
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"Stop Loss ($)": [float(amount) * (sl / 100) for amount in buy_order_amounts_quote],
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"Min Trailing Stop ($)": [float(amount) * ((ts_ap - ts_delta) / 100) for amount in buy_order_amounts_quote],
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"TP/SL Ratio": [tp / sl] * buy_order_levels,
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"TS/SL Ratio": [(ts_ap - ts_delta) / sl] * buy_order_levels,
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})
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# Create DataFrame for Sell Orders
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sell_orders_df = pd.DataFrame({
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"Level": range(1, sell_order_levels + 1),
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"Spread (%)": [dist for dist in sell_spread_distributions],
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"Amount (Quote)": sell_order_amounts_quote,
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"Take Profit ($)": [float(amount) * (tp / 100) for amount in sell_order_amounts_quote],
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"Stop Loss ($)": [float(amount) * (sl / 100) for amount in sell_order_amounts_quote],
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"Min Trailing Stop ($)": [float(amount) * ((ts_ap - ts_delta) / 100) for amount in sell_order_amounts_quote],
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"TP/SL Ratio": [tp / sl] * sell_order_levels,
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"TS/SL Ratio": [(ts_ap - ts_delta) / sl] * sell_order_levels,
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})
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# Display the DataFrames in Streamlit
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st.write("Buy Orders:")
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st.dataframe(buy_orders_df)
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st.write("Sell Orders:")
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st.dataframe(sell_orders_df)
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c1, c2, c3 = st.columns([2, 2, 1])
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with c1:
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config_base = st.text_input("Config Base", value=f"pmm_simple-{connector}-{trading_pair.split('-')[0]}")
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with c2:
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config_tag = st.text_input("Config Tag", value="1.1")
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id = f"{config_base}-{config_tag}"
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config = {
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"id": id.lower(),
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"controller_name": "pmm_simple",
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"controller_type": "market_making",
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"manual_kill_switch": None,
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"candles_config": [],
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"connector_name": connector,
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"trading_pair": trading_pair,
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"total_amount_quote": total_amount_quote,
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"buy_spreads": [Decimal(spread / 100) for spread in buy_spread_distributions],
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"sell_spreads": [Decimal(spread / 100) for spread in sell_spread_distributions],
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"buy_amounts_pct": buy_order_amounts_quote,
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"sell_amounts_pct": sell_order_amounts_quote,
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"executor_refresh_time": executor_refresh_time * 60,
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"cooldown_time": cooldown_time,
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"leverage": leverage,
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"position_mode": position_mode,
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"stop_loss": sl / 100,
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"take_profit": tp / 100,
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"time_limit": time_limit * 60,
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"take_profit_order_type": take_profit_order_type.value,
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"trailing_stop": {
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"activation_price": ts_ap / 100,
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"trailing_delta": ts_delta / 100},
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}
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yaml_config = yaml.dump(config, default_flow_style=False)
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with c3:
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download_config = st.download_button(
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label="Download YAML",
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data=yaml_config,
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file_name=f'{id.lower()}.yml',
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mime='text/yaml'
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)
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upload_config_to_backend = st.button("Upload Config to BackendAPI")
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if upload_config_to_backend:
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backend_api_client = BackendAPIClient.get_instance(host=BACKEND_API_HOST, port=BACKEND_API_PORT)
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backend_api_client.add_controller_config(config)
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st.success("Config uploaded successfully!")
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st.write("---")
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st.write("### Backtesting")
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backtesting_section(inputs, backend_api_client)
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@@ -6,8 +6,8 @@ import yaml
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from hummingbot.connector.connector_base import OrderType
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||||
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from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
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from utils.backend_api_client import BackendAPIClient
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||||
from utils.st_utils import initialize_st_page
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from backend.services.backend_api_client import BackendAPIClient
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from frontend.st_utils import initialize_st_page
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# Initialize the Streamlit page
|
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initialize_st_page(title="Trend Follower V1", icon="📈", initial_sidebar_state="expanded")
|
||||
|
||||
@@ -1,15 +1,10 @@
|
||||
from math import exp
|
||||
import streamlit as st
|
||||
from plotly.subplots import make_subplots
|
||||
import plotly.graph_objects as go
|
||||
from decimal import Decimal
|
||||
import yaml
|
||||
|
||||
from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
|
||||
from utils.backend_api_client import BackendAPIClient
|
||||
from utils.st_utils import initialize_st_page
|
||||
from hummingbot.smart_components.utils.distributions import Distributions
|
||||
|
||||
from backend.services.backend_api_client import BackendAPIClient
|
||||
from frontend.st_utils import initialize_st_page
|
||||
|
||||
# Initialize the Streamlit page
|
||||
initialize_st_page(title="XEMM Multiple Levels", icon="⚡️", initial_sidebar_state="collapsed")
|
||||
|
||||
Reference in New Issue
Block a user