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86 lines
5.2 KiB
Python
86 lines
5.2 KiB
Python
import streamlit as st
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from frontend.components.config_loader import get_default_config_loader
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from frontend.components.executors_distribution import get_executors_distribution_inputs
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from frontend.components.save_config import render_save_config
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# Import submodules
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from frontend.components.backtesting import backtesting_section
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from frontend.pages.config.pmm_dynamic.spread_and_price_multipliers import get_pmm_dynamic_multipliers
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from frontend.pages.config.pmm_dynamic.user_inputs import user_inputs
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from frontend.pages.config.utils import get_candles
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from frontend.st_utils import initialize_st_page, get_backend_api_client
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from frontend.visualization import theme
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from frontend.visualization.backtesting import create_backtesting_figure
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from frontend.visualization.candles import get_candlestick_trace
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from frontend.visualization.executors_distribution import create_executors_distribution_traces
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from frontend.visualization.backtesting_metrics import render_backtesting_metrics, render_close_types, \
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render_accuracy_metrics
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from frontend.visualization.indicators import get_macd_traces
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from frontend.visualization.utils import add_traces_to_fig
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# Initialize the Streamlit page
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initialize_st_page(title="PMM Dynamic", icon="👩🏫")
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backend_api_client = get_backend_api_client()
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# Page content
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st.text("This tool will let you create a config for PMM Dynamic, backtest and upload it to the Backend API.")
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get_default_config_loader("pmm_dynamic")
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# Get user inputs
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inputs = user_inputs()
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st.write("### Visualizing MACD and NATR indicators for PMM Dynamic")
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st.text("The MACD is used to shift the mid price and the NATR to make the spreads dynamic. "
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"In the order distributions graph, we are going to see the values of the orders affected by the average NATR")
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days_to_visualize = st.number_input("Days to Visualize", min_value=1, max_value=365, value=7)
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# Load candle data
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candles = get_candles(connector_name=inputs["candles_connector"], trading_pair=inputs["candles_trading_pair"], interval=inputs["interval"], days=days_to_visualize)
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with st.expander("Visualizing PMM Dynamic Indicators", expanded=True):
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fig = make_subplots(rows=4, cols=1, shared_xaxes=True,
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vertical_spacing=0.02, subplot_titles=('Candlestick with Bollinger Bands', 'MACD', "Price Multiplier", "Spreads Multiplier"),
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row_heights=[0.8, 0.2, 0.2, 0.2])
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add_traces_to_fig(fig, [get_candlestick_trace(candles)], row=1, col=1)
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add_traces_to_fig(fig, get_macd_traces(df=candles, macd_fast=inputs["macd_fast"], macd_slow=inputs["macd_slow"], macd_signal=inputs["macd_signal"]), row=2, col=1)
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price_multiplier, spreads_multiplier = get_pmm_dynamic_multipliers(candles, inputs["macd_fast"], inputs["macd_slow"], inputs["macd_signal"], inputs["natr_length"])
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add_traces_to_fig(fig, [go.Scatter(x=candles.index, y=price_multiplier, name="Price Multiplier", line=dict(color="blue"))], row=3, col=1)
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add_traces_to_fig(fig, [go.Scatter(x=candles.index, y=spreads_multiplier, name="Base Spread", line=dict(color="red"))], row=4, col=1)
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fig.update_layout(**theme.get_default_layout(height=1000))
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fig.update_yaxes(tickformat=".2%", row=3, col=1)
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fig.update_yaxes(tickformat=".2%", row=4, col=1)
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st.plotly_chart(fig, use_container_width=True)
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st.write("### Executors Distribution")
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st.write("The order distributions are affected by the average NATR. This means that if the first order has a spread of "
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"1 and the NATR is 0.005, the first order will have a spread of 0.5% of the mid price.")
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buy_spread_distributions, sell_spread_distributions, buy_order_amounts_pct, sell_order_amounts_pct = get_executors_distribution_inputs(use_custom_spread_units=True)
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inputs["buy_spreads"] = [spread * 100 for spread in buy_spread_distributions]
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inputs["sell_spreads"] = [spread * 100 for spread in sell_spread_distributions]
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inputs["buy_amounts_pct"] = buy_order_amounts_pct
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inputs["sell_amounts_pct"] = sell_order_amounts_pct
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st.session_state["default_config"].update(inputs)
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with st.expander("Executor Distribution:", expanded=True):
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natr_avarage = spreads_multiplier.mean()
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buy_spreads = [spread * natr_avarage for spread in inputs["buy_spreads"]]
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sell_spreads = [spread * natr_avarage for spread in inputs["sell_spreads"]]
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st.write(f"Average NATR: {natr_avarage:.2%}")
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fig = create_executors_distribution_traces(buy_spreads, sell_spreads, inputs["buy_amounts_pct"], inputs["sell_amounts_pct"], inputs["total_amount_quote"])
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st.plotly_chart(fig, use_container_width=True)
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bt_results = backtesting_section(inputs, backend_api_client)
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if bt_results:
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fig = create_backtesting_figure(
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df=bt_results["processed_data"],
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executors=bt_results["executors"],
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config=inputs)
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c1, c2 = st.columns([0.9, 0.1])
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with c1:
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render_backtesting_metrics(bt_results["results"])
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st.plotly_chart(fig, use_container_width=True)
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with c2:
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render_accuracy_metrics(bt_results["results"])
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st.write("---")
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render_close_types(bt_results["results"])
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st.write("---")
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render_save_config(st.session_state["default_config"]["id"], st.session_state["default_config"])
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