Discover our Python package designed for algorithmic trading. It brings ICT's smart money concepts to Python, offering a range of indicators for your trading strategies.
Home Page: https://pypi.org/project/smartmoneyconcepts/
License: MIT License
Create a new function to get the previous candles High and low.
You will be able to pass the timeframe of the previous candle like 1m,1H,1D,1W
This function will then get the high and low of that previous candle like the previous days high and low.
The function will then return the following data:
High - the pip the high is at.
HighBreach - When the high was breached if it has.
Low - the pip the low is at.
LowBreach - When the low was breached if it has.
100 - ((ohlc["high"][i] - top) / (bottom - top)) * 100, 1
this error needs fixing but doesn't effect performance
Create An Indicator that will determine if the candle is in a certain session.
This function must follow the sessions on https://www.babypips.com/learn/forex/forex-trading-sessions and it must account for the different time zones the data is in.
Parameters:
Retuned values:
First of all, thank you for all your efforts for the SMC Python adaptation. I am experimenting with testing this package and applying it to different strategies. However, I am having problems calculating data related to FVG and Liquidity. No matter what threshold value I enter for Liquidity, I cannot get results on the chart or as data. I also get the following error on FVG. These might be simple questions with straightforward solutions. Unfortunately, my experience with Python is at the beginner level. Thank you in advance for your support.
line 129, in
fig = add_FVG(fig)
line 39, in add_FVG
fvg_data = smc.fvg(df)
smartmoneyconcepts\smc.py", line 35, in wrap
return func(*args, **kwargs)
smartmoneyconcepts\smc.py", line 87, in fvg
mask = np.zeros(len(ohlc), dtype=np.bool)
numpy_init_.py", line 338, in getattr
raise AttributeError(former_attrs[attr])
AttributeError: module 'numpy' has no attribute 'bool'.
np.bool was a deprecated alias for the builtin bool. To avoid this error in existing code, use bool by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.bool_ here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations. Did you mean: 'bool_'?
#----------------Binance Data Fecth---------------------------------
bars = exchange.fetch_ohlcv('LINK/USDT', timeframe='4h', limit=1000)
df = pd.DataFrame(bars[:-1], columns=['timestamp', 'open', 'high', 'low', 'close', 'volume']).astype(float)
df['timestamp']=pd.to_datetime(df['timestamp'], unit='ms')
#----------------Bar Plot------------------------------------------
fig = go.Figure(
data=[
go.Candlestick(
x=df["timestamp"],
open=df["open"],
high=df["high"],
low=df["low"],
close=df["close"],
)
]
)
#----------------FVG Plot------------------------------------------
def add_FVG(fig):
fvg_data = smc.fvg(df)
# plot a rectangle for each fvg
for i in range(len(fvg_data["FVG"])):
if fvg_data["FVG"][i] != 0:
x1 = (
fvg_data["MitigatedIndex"][i]
if fvg_data["MitigatedIndex"][i] != 0
else len(df) - 1
)
fig.add_shape(
# filled Rectangle
type="rect",
x0=df.index[i],
y0=fvg_data["Top"][i],
x1=df.index[x1],
y1=fvg_data["Bottom"][i],
line=dict(
width=0,
),
fillcolor="yellow",
opacity=0.5,
)
return fig
#----------------Highs & Lows------------------------------------------
def add_highs_lows(fig):
highs_lows_data = smc.highs_lows(df,0.005)
# remove from highs_lows_data
indexs = []
levels = []
for i in range(len(highs_lows_data)):
if highs_lows_data["HighsLows"][i] != 0:
indexs.append(i)
levels.append(highs_lows_data["Levels"][i])
# plot these lines on a graph
for i in range(len(indexs) - 1):
fig.add_trace(
go.Scatter(
x=[df["timestamp"][indexs[i]], df["timestamp"][indexs[i + 1]]],
y=[levels[i], levels[i+1]],
mode="lines",
line=dict(
color="green" if highs_lows_data["HighsLows"][indexs[i]] == -1 else "red",
),
)
)
return fig
#----------------Plot Liquidity------------------------------------------
def add_liquidity(fig):
liquidity_data = smc.liquidity(df)
# draw a line horizontally for each liquidity level
for i in range(len(liquidity_data["Liquidity"])):
if liquidity_data["Liquidity"][i] != 0:
fig.add_trace(
go.Scatter(
x=[df["timestamp"][i], df["timestamp"][liquidity_data["End"][i]]],
y=[liquidity_data["Level"][i], liquidity_data["Level"][i]],
mode="lines",
line=dict(
color="orange",
),
)
)
if liquidity_data["Swept"][i] != 0:
# draw a red line between the end and the swept point
fig.add_trace(
go.Scatter(
x=[
df["timestamp"][liquidity_data["End"][i]],
df["timestamp"][liquidity_data["Swept"][i]],
],
y=[
liquidity_data["Level"][i],
(
df["high"][liquidity_data["Swept"][i]]
if liquidity_data["Liquidity"][i] == 1
else df["low"][liquidity_data["Swept"][i]]
),
],
mode="lines",
line=dict(
color="red",
),
)
)
return fig
fig = add_FVG(fig)
fig = add_highs_lows(fig)
fig = add_liquidity(fig)
fig.show()
I received the error "NameError: name 'ohlc' is not defined" when executing step smc.fvg(ohlc, join_consecutive=False)
for the sessions function, it would be better to specify the open and close time natively to the timezone that we are targeting using the pytz format. eg:
smc.sessions(df, 'New York', '09:30', '16:00', timezone= "America/New_York")
This is because the daylight savings shifts for each country is not in sync, which means that twice a year, there will be several weeks where New York is in daylight savings, but London isn't or vice versa. The same can be said of other timezones.
M
If using .shift(-X) and Series[+X], your inicaators take into account future candles data.
Create a python script to perform unit tests on all of the functions.
Hey, thanks for your great work. I have a few questions.
import pandas as pd
import plotly.graph_objects as go
import sys
import os
from binance.client import Client
from datetime import datetime
import numpy as np
import time
from smartmoneyconcepts.smc import smc
def add_swing_highs_lows(fig, swing_highs_lows_data):
indexs = []
level = []
for i in range(len(swing_highs_lows_data)):
if not np.isnan(swing_highs_lows_data["HighLow"][i]):
indexs.append(i)
level.append(swing_highs_lows_data["Level"][i])
# plot these lines on a graph
for i in range(len(indexs) - 1):
fig.add_trace(
go.Scatter(
x=[df.index[indexs[i]], df.index[indexs[i + 1]]],
y=[level[i], level[i + 1]],
mode="lines",
line=dict(
color=(
"green"
if swing_highs_lows_data["HighLow"][indexs[i]] == -1
else "red"
),
),
)
)
return fig
def add_OB(fig, ob_data):
def format_volume(volume):
if volume >= 1e12:
return f"{volume / 1e12:.3f}T"
elif volume >= 1e9:
return f"{volume / 1e9:.3f}B"
elif volume >= 1e6:
return f"{volume / 1e6:.3f}M"
elif volume >= 1e3:
return f"{volume / 1e3:.3f}k"
else:
return f"{volume:.2f}"
for i in range(len(ob_data["OB"])):
if ob_data["OB"][i] == 1:
x1 = int(
ob_data["MitigatedIndex"][i]
if ob_data["MitigatedIndex"][i] != 0
else len(df) - 1
)
fig.add_shape(
type="rect",
x0=df.index[i],
y0=ob_data["Bottom"][i],
x1=df.index[x1],
y1=ob_data["Top"][i],
line=dict(color="Green"),
fillcolor="Green",
opacity=0.3,
name="Bullish OB",
legendgroup="bullish ob",
showlegend=True,
)
if ob_data["MitigatedIndex"][i] > 0:
x_center = df.index[int(i + (ob_data["MitigatedIndex"][i] - i) / 2)]
else:
x_center = df.index[int(i + (len(df) - i) / 2)]
y_center = (ob_data["Bottom"][i] + ob_data["Top"][i]) / 2
volume_text = format_volume(ob_data["OBVolume"][i])
# Add annotation text
annotation_text = f'{volume_text} ({ob_data["Percentage"][i]}%)'
fig.add_annotation(
x=x_center,
y=y_center,
xref="x",
yref="y",
align="center",
text=annotation_text,
font=dict(color="white", size=8),
showarrow=False,
)
for i in range(len(ob_data["OB"])):
if ob_data["OB"][i] == -1:
x1 = int(
ob_data["MitigatedIndex"][i]
if ob_data["MitigatedIndex"][i] != 0
else len(df) - 1
)
fig.add_shape(
type="rect",
x0=df.index[i],
y0=ob_data["Bottom"][i],
x1=df.index[x1],
y1=ob_data["Top"][i],
line=dict(color="Red"),
fillcolor="Red",
opacity=0.3,
name="Bearish OB",
legendgroup="bearish ob",
showlegend=True,
)
if ob_data["MitigatedIndex"][i] > 0:
x_center = df.index[int(i + (ob_data["MitigatedIndex"][i] - i) / 2)]
else:
x_center = df.index[int(i + (len(df) - i) / 2)]
y_center = (ob_data["Bottom"][i] + ob_data["Top"][i]) / 2
volume_text = format_volume(ob_data["OBVolume"][i])
# Add annotation text
annotation_text = f'{volume_text} ({ob_data["Percentage"][i]}%)'
fig.add_annotation(
x=x_center,
y=y_center,
xref="x",
yref="y",
align="center",
text=annotation_text,
font=dict(color="white", size=8),
showarrow=False,
)
return fig
def import_data(symbol, start_str, timeframe):
client = Client()
start_str = str(start_str)
end_str = f"{datetime.now()}"
df = pd.DataFrame(
client.get_historical_klines(
symbol=symbol, interval=timeframe, start_str=start_str, end_str=end_str
)
).astype(float)
df = df.iloc[:, :6]
df.columns = ["timestamp", "open", "high", "low", "close", "volume"]
df = df.set_index("timestamp")
df.index = pd.to_datetime(df.index, unit="ms").strftime("%Y-%m-%d %H:%M:%S")
return df
df = import_data("STPTUSDT", "2024-05-12", "15m")
fig = go.Figure(
data=[
go.Candlestick(
x=df.index,
open=df["open"],
high=df["high"],
low=df["low"],
close=df["close"],
)
]
)
swing_highs_lows_data = smc.swing_highs_lows(df, swing_length=10)
ob_data = smc.ob(df, swing_highs_lows_data)
fig = add_OB(fig, ob_data)
fig.update_layout(xaxis_rangeslider_visible=False)
fig.update_layout(showlegend=False)
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
fig.update_xaxes(visible=False, showticklabels=False)
fig.update_yaxes(visible=False, showticklabels=False)
fig.update_layout(plot_bgcolor="rgba(0,0,0,0)")
fig.update_layout(paper_bgcolor="rgba(94, 94, 134, 1)")
fig.update_layout(font=dict(color="white"))
fig.write_image("test_binance.png")
fig
Thank you very much for your effort on this. It was a highly anticipated package for me. I am trying to install via pip however end up with an error:
ERROR: Failed building wheel for zigzag
Failed to build zigzag
ERROR: Could not build wheels for zigzag, which is required to install pyproject.toml-based projects
Do you have any suggestions?
Thank you in advance
Please add smc indicator to freqtrade
Hi,
I'm encountering an issue with the bos_choch function. Despite following the documentation and ensuring that the input dataframes are correctly formatted, the bos_choch function consistently returns an empty dataframe.
Here's a brief overview of my setup:
The intermediate outputs seem correct, but the final bos_choch result is empty. Here is a snippet of my code:
from smartmoneyconcepts import smc
dataframe.columns = [col.lower() for col in dataframe.columns]
swing_highs_lows = smc.swing_highs_lows(dataframe, swing_length=20)
if not swing_highs_lows.empty:
breakOFStructure = smc.bos_choch(dataframe, swing_highs_lows, close_break=False)
print(breakOFStructure.head())
else:
print("Swing Highs and Lows DataFrame is empty.")
I've ensured that the dataframe and swing_highs_lows contain valid data.
dataframe is belong to BTCUSDT:USDT Binance and I'm sure its contain some Break of Structures
thank you
my chart screenshot :
hint : the double x is swings points
Add a boolean parameter to the FVG function called join_consecutive.
If this is set to true then any FVG that follow after each other will be joined together.
So instead of having multiple small FVGs in a row they will be joined together and the top/bottom will be adjusted to cover all of them.
I am trying to understand the concept behind the order block detection as implemented in your code. I have reviewed the order block detection function would like to gain a deeper understanding of the logic it is based on. Could you explain the theory behind it? Additionally, I would also like to know how you define a "mitigation block" and a "breaker block" in your implementation.
Thank you for your assistance and for maintaining this amazing library.
The current chart produced by the tests is a bit messy and hard to understand.
This task is to clean up this chart make it look pretty and easy to read.
Then add the chart image to README.md file
Thank you for your great work. The library performs well overall, but there's one issue: the OB class is implemented using for loops, resulting in approximately 20 seconds of runtime. Typically, this isn't a problem, but since I'm attempting to develop a Freqtrade strategy with this library, the backtesting process takes about a day to yield results. I've attempted to use vectorized operations to improve performance, but I haven't succeeded. Could you help vectorize the classes to enhance speed? I would be extremely grateful.
Hi!
Great work!
I have a question about Volumized Order Block (tradingview script).Did you tried to convert this to python?
def add_VOB(fig):
start_time = time.time()
ob_data = smc.vob(df)
volumized_ob_time = time.time() - start_time
logger.debug(
"volumized_ob processing time (for ohlc length={}): {:.2f} seconds",
len(ob_data),
volumized_ob_time,
)
2024-02-23 12:48:40.440 | DEBUG | __main__:add_VOB:194 - volumized_ob processing time (for ohlc length=1000): 5.01 seconds
Hi,
Thanks for a great starting point on building SMC concepts using Python. I am currently trying to use your library/code to find order blocks and I was wondering if you are planning to change the ob function so it returns multiple order blocks?
For instance when I run the code on FLOWUSDT (Bybit perpetual contract) 4H candle data it detects the following order block
However, when I use the most popular order-block indicator on TW there are more OB's being displayed
I'm still getting familiar with your codebase, if I am successful in understanding it properly I can try to contribute a PR.
Add to the FVG function an argument to specify what percentage should the FVG be mitigated:
0% - Price just has to touch the FVG.
50% - Price has to reach half way.
100% - Price has to cover the whole FVG.
Hi,
is there any backtest example using a framework like freqtrade, backtesting.py, backtrader ?
import ccxt
import pandas as pd
from smartmoneyconcepts import smc
exchange = ccxt.binanceusdm(
{
"apiKey": "1",
"secret": "1",
"enableRateLimit": True,
}
)
# exchange.verbose = True
exchange.options["warnOnFetchOpenOrdersWithoutSymbol"] = False
klines = exchange.fetch_ohlcv("POLYX/USDT", "1h", limit=100)
# 转为 df
df = pd.DataFrame(
klines, columns=["timestamp", "open", "high", "low", "close", "volume"]
)
swing_highs_lows = smc.swing_highs_lows(df, 50)
print(swing_highs_lows)
# fvg_data = smc.fvg(df)
# df = pd.concat([df, fvg_data], axis=1)
# swing_highs_lows = smc.swing_highs_lows(df)
# print(swing_highs_lows)
# print(df)
Hi team,
Could you please help with some questions about bos_choch function:
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