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https://github.com/aljazceru/hummingbot-dashboard.git
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469 lines
21 KiB
Python
469 lines
21 KiB
Python
from math import exp
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import streamlit as st
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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 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 hummingbot.smart_components.utils.distributions import Distributions
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def normalize(values):
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total = sum(values)
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return [Decimal(val / total) for val in values]
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def distribution_inputs(column, dist_type_name, levels=3):
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if dist_type_name == "Spread":
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dist_type = column.selectbox(
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f"Type of {dist_type_name} Distribution",
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("Manual", "GeoCustom", "Geometric", "Fibonacci", "Logarithmic", "Arithmetic"),
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key=f"{column}_{dist_type_name.lower()}_dist_type",
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# Set the default value
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)
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else:
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dist_type = column.selectbox(
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f"Type of {dist_type_name} Distribution",
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("Manual", "Geometric", "Fibonacci", "Logarithmic", "Arithmetic"),
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key=f"{column}_{dist_type_name.lower()}_dist_type",
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# Set the default value
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)
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base, scaling_factor, step, ratio, manual_values = None, None, None, None, None
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if dist_type != "Manual":
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start = column.number_input(f"{dist_type_name} Start Value", value=1.0,
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key=f"{column}_{dist_type_name.lower()}_start")
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if dist_type == "Logarithmic":
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base = column.number_input(f"{dist_type_name} Log Base", value=exp(1),
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key=f"{column}_{dist_type_name.lower()}_base")
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scaling_factor = column.number_input(f"{dist_type_name} Scaling Factor", value=2.0,
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key=f"{column}_{dist_type_name.lower()}_scaling")
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elif dist_type == "Arithmetic":
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step = column.number_input(f"{dist_type_name} Step", value=0.1,
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key=f"{column}_{dist_type_name.lower()}_step")
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elif dist_type == "Geometric":
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ratio = column.number_input(f"{dist_type_name} Ratio", value=2.0,
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key=f"{column}_{dist_type_name.lower()}_ratio")
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elif dist_type == "GeoCustom":
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ratio = column.number_input(f"{dist_type_name} Ratio", value=2.0,
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key=f"{column}_{dist_type_name.lower()}_ratio")
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else:
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manual_values = [column.number_input(f"{dist_type_name} for level {i + 1}", value=i + 1.0,
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key=f"{column}_{dist_type_name.lower()}_{i}") for i in range(levels)]
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start = None # As start is not relevant for Manual type
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return dist_type, start, base, scaling_factor, step, ratio, manual_values
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def get_distribution(dist_type, n_levels, start, base=None, scaling_factor=None, step=None, ratio=None,
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manual_values=None):
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if dist_type == "Manual":
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return manual_values
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elif dist_type == "Linear":
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return Distributions.linear(n_levels, start, start + ts_ap)
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elif dist_type == "Fibonacci":
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return Distributions.fibonacci(n_levels, start)
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elif dist_type == "Logarithmic":
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return Distributions.logarithmic(n_levels, base, scaling_factor, start)
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elif dist_type == "Arithmetic":
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return Distributions.arithmetic(n_levels, start, step)
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elif dist_type == "Geometric":
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return Distributions.geometric(n_levels, start, ratio)
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elif dist_type == "GeoCustom":
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return [Decimal("0")] + Distributions.geometric(n_levels - 1, start, ratio)
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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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# Page content
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st.text("This tool will let you create a config for D-Man Maker V2 and upload it to the BackendAPI.")
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st.write("---")
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c1, c2, c3, c4, c5, c6, c7, c8, c9 = st.columns(9)
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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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leverage = st.number_input("Leverage", value=20)
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with c4:
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total_amount_quote = st.number_input("Total amount of quote", value=1000)
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with c5:
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position_mode = st.selectbox("Position Mode", ("HEDGE", "ONEWAY"), index=0)
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with c6:
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cooldown_time = st.number_input("Cooldown Time", value=60)
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with c7:
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executor_refresh_time = st.number_input("Refresh Time (minutes)", value=60)
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with c8:
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top_executor_refresh_time = st.number_input("Top Refresh Time (seconds)", value=60)
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with c9:
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executor_activation_bounds = st.number_input("Activation Bounds (%)", value=0.1)
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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 = [Decimal(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 = [Decimal(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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# Layout in columns
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col_dca_quote, col_tp_sl, col_levels, col_spread_dist, col_amount_dist = st.columns([1, 1, 1, 2, 2])
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buy_executor_levels = [f"BUY_{i}" for i in range(buy_order_levels)]
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sell_executor_levels = [f"SELL_{i}" for i in range(sell_order_levels)]
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with col_dca_quote:
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executor_level = st.selectbox("Executor Level", buy_executor_levels + sell_executor_levels)
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side, level = executor_level.split("_")
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if side == "BUY":
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dca_amount = buy_order_amounts_quote[int(level)]
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else:
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dca_amount = sell_order_amounts_quote[int(level)]
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st.write(f"DCA Amount: {dca_amount:.2f}")
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with col_tp_sl:
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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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sl = st.number_input("Stop Loss (%)", min_value=0.0, max_value=100.0, value=3.0, step=0.1)
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with col_levels:
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n_levels = st.number_input("Number of Levels", min_value=1, value=5)
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tp = st.number_input("Take Profit (%)", min_value=0.0, max_value=100.0, value=5.0, 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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# Spread and Amount Distributions
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spread_dist_type, spread_start, spread_base, spread_scaling, spread_step, spread_ratio, manual_spreads = distribution_inputs(
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col_spread_dist, "Spread", n_levels)
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amount_dist_type, amount_start, amount_base, amount_scaling, amount_step, amount_ratio, manual_amounts = distribution_inputs(
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col_amount_dist, "Amount", n_levels)
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spread_distribution = get_distribution(spread_dist_type, n_levels, spread_start, spread_base, spread_scaling,
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spread_step, spread_ratio, manual_spreads)
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amount_distribution = normalize(
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get_distribution(amount_dist_type, n_levels, amount_start, amount_base, amount_scaling, amount_step, amount_ratio,
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manual_amounts))
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dca_order_amounts = [Decimal(amount_dist * dca_amount) for amount_dist in amount_distribution]
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dca_spreads = [Decimal(spread - spread_distribution[0]) for spread in spread_distribution]
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break_even_values = []
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take_profit_values = []
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for level in range(n_levels):
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dca_spreads_normalized = [Decimal(spread) + Decimal(0.01) for spread in dca_spreads[:level + 1]]
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amounts = dca_order_amounts[:level + 1]
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break_even = (sum([spread * amount for spread, amount in zip(dca_spreads_normalized, amounts)]) / sum(
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amounts)) - Decimal(0.01)
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break_even_values.append(break_even)
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take_profit_values.append(break_even - Decimal(ts_ap))
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accumulated_amount = [sum(dca_order_amounts[:i + 1]) for i in range(len(dca_order_amounts))]
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def calculate_unrealized_pnl(spreads, break_even_values, accumulated_amount):
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unrealized_pnl = []
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for i in range(len(spreads)):
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distance = abs(spreads[i] - break_even_values[i])
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pnl = accumulated_amount[i] * distance / 100 # PNL calculation
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unrealized_pnl.append(pnl)
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return unrealized_pnl
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# Calculate unrealized PNL
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cum_unrealized_pnl = calculate_unrealized_pnl(dca_spreads, break_even_values, accumulated_amount)
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tech_colors = {
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'spread': '#00BFFF', # Deep Sky Blue
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'break_even': '#FFD700', # Gold
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'take_profit': '#32CD32', # Green
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'order_amount': '#1E90FF', # Dodger Blue
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'cum_amount': '#4682B4', # Steel Blue
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'stop_loss': '#FF0000', # Red
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}
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# Create Plotly figure with secondary y-axis and a dark theme
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fig = make_subplots(specs=[[{"secondary_y": True}]])
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fig.update_layout(template="plotly_dark")
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# Update the Scatter Plots and Horizontal Lines
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fig.add_trace(go.Scatter(x=list(range(len(dca_spreads))), y=dca_spreads, name='Spread (%)', mode='lines+markers',
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line=dict(width=3, color=tech_colors['spread'])), secondary_y=False)
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fig.add_trace(
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go.Scatter(x=list(range(len(break_even_values))), y=break_even_values, name='Break Even (%)', mode='lines+markers',
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line=dict(width=3, color=tech_colors['break_even'])), secondary_y=False)
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fig.add_trace(go.Scatter(x=list(range(len(take_profit_values))), y=take_profit_values, name='Take Profit (%)',
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mode='lines+markers', line=dict(width=3, color=tech_colors['take_profit'])), secondary_y=False)
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# Add the new Bar Plot for Cumulative Unrealized PNL
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fig.add_trace(go.Bar(
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x=list(range(len(cum_unrealized_pnl))),
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y=cum_unrealized_pnl,
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text=[f"{pnl:.2f}" for pnl in cum_unrealized_pnl],
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textposition='auto',
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textfont=dict(color='white', size=12),
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name='Cum Unrealized PNL',
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marker=dict(color='#FFA07A', opacity=0.6) # Light Salmon color, adjust as needed
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), secondary_y=True)
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fig.add_trace(go.Bar(
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x=list(range(len(dca_order_amounts))),
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y=dca_order_amounts,
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text=[f"{amt:.2f}" for amt in dca_order_amounts], # List comprehension to format text labels
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textposition='auto',
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textfont=dict(
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color='white',
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size=12
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),
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name='Order Amount',
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marker=dict(color=tech_colors['order_amount'], opacity=0.5),
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), secondary_y=True)
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# Modify the Bar Plot for Accumulated Amount
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fig.add_trace(go.Bar(
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x=list(range(len(accumulated_amount))),
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y=accumulated_amount,
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text=[f"{amt:.2f}" for amt in accumulated_amount], # List comprehension to format text labels
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textposition='auto',
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textfont=dict(
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color='white',
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size=12
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),
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name='Cum Amount',
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marker=dict(color=tech_colors['cum_amount'], opacity=0.5),
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), secondary_y=True)
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# Add Horizontal Lines for Last Breakeven Price and Stop Loss Level
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last_break_even = break_even_values[-1]
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stop_loss_value = last_break_even + Decimal(sl)
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# Horizontal Lines for Last Breakeven and Stop Loss
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fig.add_hline(y=last_break_even, line_dash="dash", annotation_text=f"Global Break Even: {last_break_even:.2f} (%)",
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annotation_position="top left", line_color=tech_colors['break_even'])
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fig.add_hline(y=stop_loss_value, line_dash="dash", annotation_text=f"Stop Loss: {stop_loss_value:.2f} (%)",
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annotation_position="bottom right", line_color=tech_colors['stop_loss'])
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# Update Annotations for Spread and Break Even
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for i, (spread, be_value, tp_value) in enumerate(zip(dca_spreads, break_even_values, take_profit_values)):
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fig.add_annotation(x=i, y=spread, text=f"{spread:.2f}%", showarrow=True, arrowhead=1, yshift=10, xshift=-2,
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font=dict(color=tech_colors['spread']))
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fig.add_annotation(x=i, y=be_value, text=f"{be_value:.2f}%", showarrow=True, arrowhead=1, yshift=5, xshift=-2,
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font=dict(color=tech_colors['break_even']))
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fig.add_annotation(x=i, y=tp_value, text=f"{tp_value:.2f}%", showarrow=True, arrowhead=1, yshift=10, xshift=-2,
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font=dict(color=tech_colors['take_profit']))
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# Update Layout with a Dark Theme
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fig.update_layout(
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title="Spread, Accumulated Amount, Break Even, and Take Profit by Order Level",
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xaxis_title="Order Level",
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yaxis_title="Spread (%)",
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yaxis2_title="Amount (Quote)",
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height=800,
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width=1800,
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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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font=dict(color='white') # Font color
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)
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# Calculate metrics
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dca_max_loss = dca_amount * Decimal(sl / 100)
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profit_per_level = [cum_amount * Decimal(ts_ap / 100) for cum_amount in accumulated_amount]
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loots_to_recover = [dca_max_loss / profit for profit in profit_per_level]
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# Define a consistent annotation size and maximum value for the secondary y-axis
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circle_text = "●" # Unicode character for a circle
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max_secondary_value = max(max(accumulated_amount), max(dca_order_amounts),
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max(cum_unrealized_pnl)) # Adjust based on your secondary y-axis data
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# Determine an appropriate y-offset for annotations
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y_offset_secondary = max_secondary_value * Decimal(
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0.1) # Adjusts the height relative to the maximum value on the secondary y-axis
|
|
|
|
# Add annotations to the Plotly figure for the secondary y-axis
|
|
for i, loot in enumerate(loots_to_recover):
|
|
fig.add_annotation(
|
|
x=i,
|
|
y=max_secondary_value + y_offset_secondary, # Position above the maximum value using the offset
|
|
text=f"{circle_text}<br>LTR: {round(loot, 2)}", # Circle symbol and loot value in separate lines
|
|
showarrow=False,
|
|
font=dict(size=16, color='purple'),
|
|
xanchor="center", # Centers the text above the x coordinate
|
|
yanchor="bottom", # Anchors the text at its bottom to avoid overlapping
|
|
align="center",
|
|
yref="y2" # Reference the secondary y-axis
|
|
)
|
|
# Add Max Loss Metric as an Annotation
|
|
dca_max_loss_annotation_text = f"DCA Max Loss (Quote): {dca_max_loss:.2f}"
|
|
fig.add_annotation(
|
|
x=max(len(dca_spreads), len(break_even_values)) - 1, # Positioning the annotation to the right
|
|
text=dca_max_loss_annotation_text,
|
|
showarrow=False,
|
|
font=dict(size=20, color='white'),
|
|
bgcolor='red', # Red background for emphasis
|
|
xanchor="left",
|
|
yanchor="top",
|
|
yref="y2" # Reference the secondary y-axis
|
|
)
|
|
|
|
st.write("---")
|
|
|
|
# Display in Streamlit
|
|
st.plotly_chart(fig)
|
|
c1, c2, c3 = st.columns([2, 2, 1])
|
|
with c1:
|
|
config_base = st.text_input("Config Base", value=f"{connector}-{trading_pair.split('-')[0]}")
|
|
with c2:
|
|
config_tag = st.text_input("Config Tag", value="1.1")
|
|
|
|
id = f"{config_base}-{config_tag}"
|
|
config = {
|
|
"id": id.lower(),
|
|
"controller_name": "dman_maker_v2",
|
|
"controller_type": "market_making",
|
|
"manual_kill_switch": None,
|
|
"candles_config": [],
|
|
"connector_name": connector,
|
|
"trading_pair": trading_pair,
|
|
"total_amount_quote": total_amount_quote,
|
|
"buy_spreads": [spread / 100 for spread in buy_spread_distributions],
|
|
"sell_spreads": [spread / 100 for spread in sell_spread_distributions],
|
|
"buy_amounts_pct": buy_order_amounts_quote,
|
|
"sell_amounts_pct": sell_order_amounts_quote,
|
|
"executor_refresh_time": executor_refresh_time * 60,
|
|
"cooldown_time": cooldown_time,
|
|
"leverage": leverage,
|
|
"position_mode": position_mode,
|
|
"stop_loss": sl / 100,
|
|
"take_profit": tp / 100,
|
|
"time_limit": time_limit * 60,
|
|
"take_profit_order_type": 2,
|
|
"trailing_stop": {
|
|
"activation_price": ts_ap / 100,
|
|
"trailing_delta": ts_delta / 100},
|
|
"dca_amounts": dca_order_amounts,
|
|
"dca_spreads": [spread / 100 for spread in dca_spreads],
|
|
"top_executor_refresh_time": top_executor_refresh_time,
|
|
"executor_activation_bounds": [executor_activation_bounds / 100],
|
|
}
|
|
yaml_config = yaml.dump(config, default_flow_style=False)
|
|
|
|
with c3:
|
|
download_config = st.download_button(
|
|
label="Download YAML",
|
|
data=yaml_config,
|
|
file_name=f'{id.lower()}.yml',
|
|
mime='text/yaml'
|
|
)
|
|
upload_config_to_backend = st.button("Upload Config to BackendAPI")
|
|
|
|
|
|
if upload_config_to_backend:
|
|
backend_api_client = BackendAPIClient.get_instance(host=BACKEND_API_HOST, port=BACKEND_API_PORT)
|
|
backend_api_client.add_controller_config(config)
|
|
st.success("Config uploaded successfully!") |