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(feat) update mm controllers

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This commit is contained in:
cardosofede
2024-07-23 15:02:00 +03:00
parent 4c4abd3307
commit 681b75055d
7 changed files with 46 additions and 39 deletions
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11
pages/config/dman_maker_v2/app.py
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@@ -1,16 +1,13 @@
import streamlit as st
from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
from backend.services.backend_api_client import BackendAPIClient
from frontend.components.backtesting import backtesting_section
from frontend.components.config_loader import get_default_config_loader
from frontend.components.dca_distribution import get_dca_distribution_inputs
from frontend.components.save_config import render_save_config
from frontend.pages.config.dman_maker_v2.user_inputs import user_inputs
from frontend.st_utils import initialize_st_page, get_backend_api_client
from frontend.st_utils import get_backend_api_client, initialize_st_page
from frontend.visualization.backtesting import create_backtesting_figure
from frontend.visualization.backtesting_metrics import render_backtesting_metrics, render_accuracy_metrics, \
render_close_types
from frontend.visualization.backtesting_metrics import render_accuracy_metrics, render_backtesting_metrics, render_close_types
from frontend.visualization.dca_builder import create_dca_graph
from frontend.visualization.executors_distribution import create_executors_distribution_traces
@@ -18,14 +15,14 @@ from frontend.visualization.executors_distribution import create_executors_distr
initialize_st_page(title="D-Man Maker V2", icon="🧙‍♂️")
backend_api_client = get_backend_api_client()
# Page content
st.text("This tool will let you create a config for D-Man Maker V2 and upload it to the BackendAPI.")
get_default_config_loader("dman_maker_v2")
inputs = user_inputs()
with st.expander("Executor Distribution:", expanded=True):
fig = create_executors_distribution_traces(inputs["buy_spreads"], inputs["sell_spreads"], inputs["buy_amounts_pct"], inputs["sell_amounts_pct"], inputs["total_amount_quote"])
fig = create_executors_distribution_traces(inputs["buy_spreads"], inputs["sell_spreads"], inputs["buy_amounts_pct"],
inputs["sell_amounts_pct"], inputs["total_amount_quote"])
st.plotly_chart(fig, use_container_width=True)
dca_inputs = get_dca_distribution_inputs()

6
pages/config/dman_maker_v2/user_inputs.py
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@@ -5,8 +5,10 @@ from frontend.components.market_making_general_inputs import get_market_making_g
def user_inputs():
connector_name, trading_pair, leverage, total_amount_quote, position_mode, cooldown_time, executor_refresh_time, _, _, _ = get_market_making_general_inputs()
buy_spread_distributions, sell_spread_distributions, buy_order_amounts_pct, sell_order_amounts_pct = get_executors_distribution_inputs()
connector_name, trading_pair, leverage, total_amount_quote, position_mode, cooldown_time,\
executor_refresh_time, _, _, _ = get_market_making_general_inputs()
buy_spread_distributions, sell_spread_distributions, buy_order_amounts_pct, \
sell_order_amounts_pct = get_executors_distribution_inputs()
with st.expander("Custom D-Man Maker V2 Settings"):
c1, c2 = st.columns(2)
with c1:

41
pages/config/pmm_dynamic/app.py
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@@ -1,23 +1,21 @@
import streamlit as st
import plotly.graph_objects as go
import streamlit as st
from plotly.subplots import make_subplots
from frontend.components.config_loader import get_default_config_loader
from frontend.components.executors_distribution import get_executors_distribution_inputs
from frontend.components.save_config import render_save_config
# Import submodules
from frontend.components.backtesting import backtesting_section
from frontend.components.config_loader import get_default_config_loader
from frontend.components.executors_distribution import get_executors_distribution_inputs
from frontend.components.save_config import render_save_config
from frontend.pages.config.pmm_dynamic.spread_and_price_multipliers import get_pmm_dynamic_multipliers
from frontend.pages.config.pmm_dynamic.user_inputs import user_inputs
from frontend.pages.config.utils import get_candles
from frontend.st_utils import initialize_st_page, get_backend_api_client
from frontend.st_utils import get_backend_api_client, initialize_st_page
from frontend.visualization import theme
from frontend.visualization.backtesting import create_backtesting_figure
from frontend.visualization.backtesting_metrics import render_accuracy_metrics, render_backtesting_metrics, render_close_types
from frontend.visualization.candles import get_candlestick_trace
from frontend.visualization.executors_distribution import create_executors_distribution_traces
from frontend.visualization.backtesting_metrics import render_backtesting_metrics, render_close_types, \
render_accuracy_metrics
from frontend.visualization.indicators import get_macd_traces
from frontend.visualization.utils import add_traces_to_fig
@@ -35,16 +33,25 @@ st.text("The MACD is used to shift the mid price and the NATR to make the spread
"In the order distributions graph, we are going to see the values of the orders affected by the average NATR")
days_to_visualize = st.number_input("Days to Visualize", min_value=1, max_value=365, value=7)
# Load candle data
candles = get_candles(connector_name=inputs["candles_connector"], trading_pair=inputs["candles_trading_pair"], interval=inputs["interval"], days=days_to_visualize)
candles = get_candles(connector_name=inputs["candles_connector"], trading_pair=inputs["candles_trading_pair"],
interval=inputs["interval"], days=days_to_visualize)
with st.expander("Visualizing PMM Dynamic Indicators", expanded=True):
fig = make_subplots(rows=4, cols=1, shared_xaxes=True,
vertical_spacing=0.02, subplot_titles=('Candlestick with Bollinger Bands', 'MACD', "Price Multiplier", "Spreads Multiplier"),
vertical_spacing=0.02, subplot_titles=("Candlestick with Bollinger Bands", "MACD",
"Price Multiplier", "Spreads Multiplier"),
row_heights=[0.8, 0.2, 0.2, 0.2])
add_traces_to_fig(fig, [get_candlestick_trace(candles)], row=1, col=1)
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)
price_multiplier, spreads_multiplier = get_pmm_dynamic_multipliers(candles, inputs["macd_fast"], inputs["macd_slow"], inputs["macd_signal"], inputs["natr_length"])
add_traces_to_fig(fig, [go.Scatter(x=candles.index, y=price_multiplier, name="Price Multiplier", line=dict(color="blue"))], row=3, col=1)
add_traces_to_fig(fig, [go.Scatter(x=candles.index, y=spreads_multiplier, name="Base Spread", line=dict(color="red"))], row=4, col=1)
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)
price_multiplier, spreads_multiplier = get_pmm_dynamic_multipliers(candles, inputs["macd_fast"],
inputs["macd_slow"], inputs["macd_signal"],
inputs["natr_length"])
add_traces_to_fig(fig, [
go.Scatter(x=candles.index, y=price_multiplier, name="Price Multiplier", line=dict(color="blue"))], row=3,
col=1)
add_traces_to_fig(fig,
[go.Scatter(x=candles.index, y=spreads_multiplier, name="Base Spread", line=dict(color="red"))],
row=4, col=1)
fig.update_layout(**theme.get_default_layout(height=1000))
fig.update_yaxes(tickformat=".2%", row=3, col=1)
fig.update_yaxes(tickformat=".2%", row=4, col=1)
@@ -53,7 +60,8 @@ with st.expander("Visualizing PMM Dynamic Indicators", expanded=True):
st.write("### Executors Distribution")
st.write("The order distributions are affected by the average NATR. This means that if the first order has a spread of "
"1 and the NATR is 0.005, the first order will have a spread of 0.5% of the mid price.")
buy_spread_distributions, sell_spread_distributions, buy_order_amounts_pct, sell_order_amounts_pct = get_executors_distribution_inputs(use_custom_spread_units=True)
buy_spread_distributions, sell_spread_distributions, buy_order_amounts_pct, \
sell_order_amounts_pct = get_executors_distribution_inputs(use_custom_spread_units=True)
inputs["buy_spreads"] = [spread * 100 for spread in buy_spread_distributions]
inputs["sell_spreads"] = [spread * 100 for spread in sell_spread_distributions]
inputs["buy_amounts_pct"] = buy_order_amounts_pct
@@ -64,7 +72,8 @@ with st.expander("Executor Distribution:", expanded=True):
buy_spreads = [spread * natr_avarage for spread in inputs["buy_spreads"]]
sell_spreads = [spread * natr_avarage for spread in inputs["sell_spreads"]]
st.write(f"Average NATR: {natr_avarage:.2%}")
fig = create_executors_distribution_traces(buy_spreads, sell_spreads, inputs["buy_amounts_pct"], inputs["sell_amounts_pct"], inputs["total_amount_quote"])
fig = create_executors_distribution_traces(buy_spreads, sell_spreads, inputs["buy_amounts_pct"],
inputs["sell_amounts_pct"], inputs["total_amount_quote"])
st.plotly_chart(fig, use_container_width=True)
bt_results = backtesting_section(inputs, backend_api_client)

2
pages/config/pmm_dynamic/spread_and_price_multipliers.py
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@@ -1,4 +1,4 @@
import pandas_ta as ta # noqa: F401
import pandas_ta as ta # noqa: F401
def get_pmm_dynamic_multipliers(df, macd_fast, macd_slow, macd_signal, natr_length):

3
pages/config/pmm_dynamic/user_inputs.py
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@@ -10,7 +10,8 @@ def user_inputs():
macd_slow = default_config.get("macd_slow", 42)
macd_signal = default_config.get("macd_signal", 9)
natr_length = default_config.get("natr_length", 14)
connector_name, trading_pair, leverage, total_amount_quote, position_mode, cooldown_time, executor_refresh_time, candles_connector, candles_trading_pair, interval = get_market_making_general_inputs(custom_candles=True)
connector_name, trading_pair, leverage, total_amount_quote, position_mode, cooldown_time, executor_refresh_time, \
candles_connector, candles_trading_pair, interval = get_market_making_general_inputs(custom_candles=True)
sl, tp, time_limit, ts_ap, ts_delta, take_profit_order_type = get_risk_management_inputs()
with st.expander("PMM Dynamic Configuration", expanded=True):
c1, c2, c3, c4 = st.columns(4)

14
pages/config/pmm_simple/app.py
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@@ -1,23 +1,20 @@
import streamlit as st
from backend.services.backend_api_client import BackendAPIClient
from CONFIG import BACKEND_API_HOST, BACKEND_API_PORT
from frontend.components.backtesting import backtesting_section
from frontend.components.config_loader import get_default_config_loader
from frontend.components.save_config import render_save_config
# Import submodules
from frontend.pages.config.pmm_simple.user_inputs import user_inputs
from frontend.components.backtesting import backtesting_section
from frontend.st_utils import initialize_st_page, get_backend_api_client
from frontend.st_utils import get_backend_api_client, initialize_st_page
from frontend.visualization.backtesting import create_backtesting_figure
from frontend.visualization.backtesting_metrics import render_accuracy_metrics, render_backtesting_metrics, render_close_types
from frontend.visualization.executors_distribution import create_executors_distribution_traces
from frontend.visualization.backtesting_metrics import render_backtesting_metrics, render_close_types, \
render_accuracy_metrics
# Initialize the Streamlit page
initialize_st_page(title="PMM Simple", icon="👨‍🏫")
backend_api_client = get_backend_api_client()
# Page content
st.text("This tool will let you create a config for PMM Simple, backtest and upload it to the Backend API.")
get_default_config_loader("pmm_simple")
@@ -26,7 +23,8 @@ inputs = user_inputs()
st.session_state["default_config"].update(inputs)
with st.expander("Executor Distribution:", expanded=True):
fig = create_executors_distribution_traces(inputs["buy_spreads"], inputs["sell_spreads"], inputs["buy_amounts_pct"], inputs["sell_amounts_pct"], inputs["total_amount_quote"])
fig = create_executors_distribution_traces(inputs["buy_spreads"], inputs["sell_spreads"], inputs["buy_amounts_pct"],
inputs["sell_amounts_pct"], inputs["total_amount_quote"])
st.plotly_chart(fig, use_container_width=True)
bt_results = backtesting_section(inputs, backend_api_client)

8
pages/config/pmm_simple/user_inputs.py
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@@ -1,13 +1,13 @@
import streamlit as st
from frontend.components.executors_distribution import get_executors_distribution_inputs
from frontend.components.market_making_general_inputs import get_market_making_general_inputs
from frontend.components.risk_management import get_risk_management_inputs
def user_inputs():
connector_name, trading_pair, leverage, total_amount_quote, position_mode, cooldown_time, executor_refresh_time, _, _, _ = get_market_making_general_inputs()
buy_spread_distributions, sell_spread_distributions, buy_order_amounts_pct, sell_order_amounts_pct = get_executors_distribution_inputs()
connector_name, trading_pair, leverage, total_amount_quote, position_mode, cooldown_time, \
executor_refresh_time, _, _, _ = get_market_making_general_inputs()
buy_spread_distributions, sell_spread_distributions, buy_order_amounts_pct, \
sell_order_amounts_pct = get_executors_distribution_inputs()
sl, tp, time_limit, ts_ap, ts_delta, take_profit_order_type = get_risk_management_inputs()
# Create the config
config = {