diff --git a/main.py b/main.py index 42aa7cf0..cebdbb81 100644 --- a/main.py +++ b/main.py @@ -14,6 +14,7 @@ def main_page(): Page("pages/master_conf/app.py", "Credentials", "🗝️"), Page("pages/bot_orchestration/app.py", "Instances", "🦅"), Page("pages/file_manager/app.py", "File Explorer", "🗂"), + Page("pages/position_builder/app.py", "Position Builder", "🔭"), Section("Backtest Manager", "⚙️"), Page("pages/backtest_get_data/app.py", "Get Data", "💾"), Page("pages/backtest_create/create.py", "Create", "⚔️"), diff --git a/pages/position_builder/README.md b/pages/position_builder/README.md new file mode 100644 index 00000000..e69de29b diff --git a/pages/position_builder/app.py b/pages/position_builder/app.py new file mode 100644 index 00000000..7667caa0 --- /dev/null +++ b/pages/position_builder/app.py @@ -0,0 +1,266 @@ +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 utils.st_utils import initialize_st_page +from hummingbot.smart_components.utils.distributions import Distributions + +# Initialize the Streamlit page +initialize_st_page(title="Position Generator", icon="🔭", initial_sidebar_state="collapsed") + +# Page content +st.text("This tool will help you analyze and generate a position config.") +st.write("---") + +# Layout in columns +col_quote, col_tp_sl, col_levels, col_spread_dist, col_amount_dist = st.columns([1, 1, 1, 2, 2]) + + +def normalize(values): + total = sum(values) + return [val / total for val in values] + + +def convert_to_yaml(spreads, order_amounts): + data = { + 'dca_spreads': [float(spread)/100 for spread in spreads], + 'dca_amounts': [float(amount) for amount in order_amounts] + } + return yaml.dump(data, default_flow_style=False) + + +with col_quote: + total_amount_quote = st.number_input("Total amount of quote", value=1000) + +with col_tp_sl: + tp = st.number_input("Take Profit (%)", min_value=0.0, max_value=100.0, value=2.0, step=0.1) + sl = st.number_input("Stop Loss (%)", min_value=0.0, max_value=100.0, value=8.0, step=0.1) + +with col_levels: + n_levels = st.number_input("Number of Levels", min_value=1, value=5) + + +def distribution_inputs(column, dist_type_name): + if dist_type_name == "Spread": + dist_type = column.selectbox( + f"Type of {dist_type_name} Distribution", + ("GeoCustom", "Geometric", "Fibonacci", "Manual", "Logarithmic", "Arithmetic"), + key=f"{dist_type_name.lower()}_dist_type", + # Set the default value + ) + else: + dist_type = column.selectbox( + f"Type of {dist_type_name} Distribution", + ("Geometric", "Fibonacci", "Manual", "Logarithmic", "Arithmetic"), + key=f"{dist_type_name.lower()}_dist_type", + # Set the default value + ) + base, scaling_factor, step, ratio, manual_values = None, None, None, None, None + + if dist_type != "Manual": + start = column.number_input(f"{dist_type_name} Start Value", value=1.0, key=f"{dist_type_name.lower()}_start") + if dist_type == "Logarithmic": + base = column.number_input(f"{dist_type_name} Log Base", value=exp(1), key=f"{dist_type_name.lower()}_base") + scaling_factor = column.number_input(f"{dist_type_name} Scaling Factor", value=2.0, key=f"{dist_type_name.lower()}_scaling") + elif dist_type == "Arithmetic": + step = column.number_input(f"{dist_type_name} Step", value=0.1, key=f"{dist_type_name.lower()}_step") + elif dist_type == "Geometric": + ratio = column.number_input(f"{dist_type_name} Ratio", value=2.0, key=f"{dist_type_name.lower()}_ratio") + elif dist_type == "GeoCustom": + ratio = column.number_input(f"{dist_type_name} Ratio", value=2.0, key=f"{dist_type_name.lower()}_ratio") + else: + manual_values = [column.number_input(f"{dist_type_name} for level {i+1}", value=1.0, key=f"{dist_type_name.lower()}_{i}") for i in range(n_levels)] + start = None # As start is not relevant for Manual type + + return dist_type, start, base, scaling_factor, step, ratio, manual_values + + +# Spread and Amount Distributions +spread_dist_type, spread_start, spread_base, spread_scaling, spread_step, spread_ratio, manual_spreads = distribution_inputs(col_spread_dist, "Spread") +amount_dist_type, amount_start, amount_base, amount_scaling, amount_step, amount_ratio, manual_amounts = distribution_inputs(col_amount_dist, "Amount") + + +def get_distribution(dist_type, n_levels, start, base=None, scaling_factor=None, step=None, ratio=None, manual_values=None): + if dist_type == "Manual": + return manual_values + elif dist_type == "Linear": + return Distributions.linear(n_levels, start, start + tp) + elif dist_type == "Fibonacci": + return Distributions.fibonacci(n_levels, start) + elif dist_type == "Logarithmic": + return Distributions.logarithmic(n_levels, base, scaling_factor, start) + elif dist_type == "Arithmetic": + return Distributions.arithmetic(n_levels, start, step) + elif dist_type == "Geometric": + return Distributions.geometric(n_levels, start, ratio) + elif dist_type == "GeoCustom": + return [Decimal("0")] + Distributions.geometric(n_levels - 1, start, ratio) + +spread_distribution = get_distribution(spread_dist_type, n_levels, spread_start, spread_base, spread_scaling, spread_step, spread_ratio, manual_spreads) +amount_distribution = normalize(get_distribution(amount_dist_type, n_levels, amount_start, amount_base, amount_scaling, amount_step, amount_ratio, manual_amounts)) +order_amounts = [Decimal(amount_dist * total_amount_quote) for amount_dist in amount_distribution] +spreads = [Decimal(spread - spread_distribution[0]) for spread in spread_distribution] + + +# Export Button +if st.button('Export as YAML'): + yaml_data = convert_to_yaml(spreads, order_amounts) + st.download_button( + label="Download YAML", + data=yaml_data, + file_name='config.yaml', + mime='text/yaml' + ) + +break_even_values = [] +take_profit_values = [] +for level in range(n_levels): + spreads_normalized = [Decimal(spread) + Decimal(0.01) for spread in spreads[:level+1]] + amounts = order_amounts[:level+1] + break_even = (sum([spread * amount for spread, amount in zip(spreads_normalized, amounts)]) / sum(amounts)) - Decimal(0.01) + break_even_values.append(break_even) + take_profit_values.append(break_even - Decimal(tp)) + +accumulated_amount = [sum(order_amounts[:i+1]) for i in range(len(order_amounts))] + + +def calculate_unrealized_pnl(spreads, break_even_values, accumulated_amount): + unrealized_pnl = [] + for i in range(len(spreads)): + distance = abs(spreads[i] - break_even_values[i]) + pnl = accumulated_amount[i] * distance / 100 # PNL calculation + unrealized_pnl.append(pnl) + return unrealized_pnl + +# Calculate unrealized PNL +cum_unrealized_pnl = calculate_unrealized_pnl(spreads, break_even_values, accumulated_amount) + + +tech_colors = { + 'spread': '#00BFFF', # Deep Sky Blue + 'break_even': '#FFD700', # Gold + 'take_profit': '#32CD32', # Green + 'order_amount': '#1E90FF', # Dodger Blue + 'cum_amount': '#4682B4', # Steel Blue + 'stop_loss': '#FF0000', # Red +} + +# Create Plotly figure with secondary y-axis and a dark theme +fig = make_subplots(specs=[[{"secondary_y": True}]]) +fig.update_layout(template="plotly_dark") + +# Update the Scatter Plots and Horizontal Lines +fig.add_trace(go.Scatter(x=list(range(len(spreads))), y=spreads, name='Spread (%)', mode='lines+markers', line=dict(width=3, color=tech_colors['spread'])), secondary_y=False) +fig.add_trace(go.Scatter(x=list(range(len(break_even_values))), y=break_even_values, name='Break Even (%)', mode='lines+markers', line=dict(width=3, color=tech_colors['break_even'])), secondary_y=False) +fig.add_trace(go.Scatter(x=list(range(len(take_profit_values))), y=take_profit_values, name='Take Profit (%)', mode='lines+markers', line=dict(width=3, color=tech_colors['take_profit'])), secondary_y=False) + +# Add the new Bar Plot for Cumulative Unrealized PNL +fig.add_trace(go.Bar( + x=list(range(len(cum_unrealized_pnl))), + y=cum_unrealized_pnl, + text=[f"{pnl:.2f}" for pnl in cum_unrealized_pnl], + textposition='auto', + textfont=dict(color='white', size=12), + name='Cum Unrealized PNL', + marker=dict(color='#FFA07A', opacity=0.6) # Light Salmon color, adjust as needed +), secondary_y=True) + +fig.add_trace(go.Bar( + x=list(range(len(order_amounts))), + y=order_amounts, + text=[f"{amt:.2f}" for amt in order_amounts], # List comprehension to format text labels + textposition='auto', + textfont=dict( + color='white', + size=12 + ), + name='Order Amount', + marker=dict(color=tech_colors['order_amount'], opacity=0.5), +), secondary_y=True) + +# Modify the Bar Plot for Accumulated Amount +fig.add_trace(go.Bar( + x=list(range(len(accumulated_amount))), + y=accumulated_amount, + text=[f"{amt:.2f}" for amt in accumulated_amount], # List comprehension to format text labels + textposition='auto', + textfont=dict( + color='white', + size=12 + ), + name='Cum Amount', + marker=dict(color=tech_colors['cum_amount'], opacity=0.5), +), secondary_y=True) + + +# Add Horizontal Lines for Last Breakeven Price and Stop Loss Level +last_break_even = break_even_values[-1] +stop_loss_value = last_break_even + Decimal(sl) +# Horizontal Lines for Last Breakeven and Stop Loss +fig.add_hline(y=last_break_even, line_dash="dash", annotation_text=f"Global Break Even: {last_break_even:.2f} (%)", annotation_position="top left", line_color=tech_colors['break_even']) +fig.add_hline(y=stop_loss_value, line_dash="dash", annotation_text=f"Stop Loss: {stop_loss_value:.2f} (%)", annotation_position="bottom right", line_color=tech_colors['stop_loss']) + +# Update Annotations for Spread and Break Even +for i, (spread, be_value, tp_value) in enumerate(zip(spreads, break_even_values, take_profit_values)): + fig.add_annotation(x=i, y=spread, text=f"{spread:.2f}%", showarrow=True, arrowhead=1, yshift=10, xshift=-2, font=dict(color=tech_colors['spread'])) + fig.add_annotation(x=i, y=be_value, text=f"{be_value:.2f}%", showarrow=True, arrowhead=1, yshift=5, xshift=-2, font=dict(color=tech_colors['break_even'])) + fig.add_annotation(x=i, y=tp_value, text=f"{tp_value:.2f}%", showarrow=True, arrowhead=1, yshift=10, xshift=-2, font=dict(color=tech_colors['take_profit'])) +# Update Layout with a Dark Theme +fig.update_layout( + title="Spread, Accumulated Amount, Break Even, and Take Profit by Order Level", + xaxis_title="Order Level", + yaxis_title="Spread (%)", + yaxis2_title="Amount (Quote)", + height=800, + width=1800, + plot_bgcolor='rgba(0, 0, 0, 0)', # Transparent background + paper_bgcolor='rgba(0, 0, 0, 0.1)', # Lighter shade for the paper + font=dict(color='white') # Font color +) + +# Calculate metrics +max_loss = total_amount_quote * Decimal(sl / 100) +profit_per_level = [cum_amount * Decimal(tp / 100) for cum_amount in accumulated_amount] +loots_to_recover = [max_loss / profit for profit in profit_per_level] + +# Define a consistent annotation size and maximum value for the secondary y-axis +circle_text = "●" # Unicode character for a circle +max_secondary_value = max(max(accumulated_amount), max(order_amounts), max(cum_unrealized_pnl)) # Adjust based on your secondary y-axis data + +# Determine an appropriate y-offset for annotations +y_offset_secondary = max_secondary_value * Decimal(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}
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 +max_loss_annotation_text = f"Max Loss (Quote): {max_loss:.2f}" +fig.add_annotation( + x=max(len(spreads), len(break_even_values)) - 1, # Positioning the annotation to the right + text=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) +