Python version of the indicators I developed for cTrader trading plataform, with improvements (paralel processing) and additional features compared to C# source code.
Only order_flow_ticks and order_flow_aggregated are plotted using the plotly library, see 'notebooks' folder.
All others are plotted with mplfinance.
import pandas as pd
from order_flow_ticks import OrderFlowTicks
df_ticks = pd.read_parquet(f"data/US30_T1_2025_cT.parquet")
df_ticks.rename(columns={'bid': 'close'}, inplace=True)
df_ticks.head(3)
df_ticks.tail(3)
len(df_ticks)| ask | close | spread | |
|---|---|---|---|
| datetime | |||
| 2025-05-15 00:00:00.650 | 41839.4 | 41838.3 | 1.1 |
| 2025-05-15 00:00:01.002 | 41839.8 | 41838.7 | 1.1 |
| 2025-05-15 00:00:01.252 | 41840.2 | 41839.1 | 1.1 |
| ask | close | spread | |
|---|---|---|---|
| datetime | |||
| 2025-05-16 20:54:59.640 | 42376.7 | 42375.6 | 1.1 |
| 2025-05-16 20:54:59.841 | 42377.1 | 42376.0 | 1.1 |
| 2025-05-16 20:55:00.000 | 42377.9 | 42376.8 | 1.1 |
180399
df_ohlcv = pd.read_parquet(f"data/US30_1Minute_2025_cT.parquet")
df_ohlcv.head(3)
df_ohlcv.tail(3)
len(df_ohlcv)| open | high | low | close | volume | |
|---|---|---|---|---|---|
| datetime | |||||
| 2025-05-15 00:00:00 | 41838.7 | 41846.1 | 41833.8 | 41846.1 | 142.0 |
| 2025-05-15 00:01:00 | 41845.1 | 41853.3 | 41843.8 | 41852.1 | 90.0 |
| 2025-05-15 00:02:00 | 41853.5 | 41858.6 | 41853.5 | 41855.1 | 96.0 |
| open | high | low | close | volume | |
|---|---|---|---|---|---|
| datetime | |||||
| 2025-05-16 20:52:00 | 42462.1 | 42465.6 | 42447.1 | 42447.3 | 205.0 |
| 2025-05-16 20:53:00 | 42447.7 | 42449.8 | 42412.2 | 42414.2 | 260.0 |
| 2025-05-16 20:54:00 | 42412.5 | 42420.4 | 42371.4 | 42376.0 | 399.0 |
2631
from volume_profile import VolumeProfile, DistributionData
from tpo_profile import TpoProfile
from multi_vwap import MultiVwap
vp = VolumeProfile(df_ohlcv, None, 7, pd.Timedelta(hours=8), DistributionData.OHLC_No_Avg)
tpo = TpoProfile(df_ohlcv, 7, pd.Timedelta(hours=8))
vwap = MultiVwap(df_ohlcv)vp.plot('buy_sell')
tpo.plot()
vwap.plot('2025-05-15 14:33:00')
df_renko = pd.read_parquet(f"data/2023/US30_Re50_2023_normal.parquet")
from weis_wyckoff_system import WeisWyckoffSystem
ww = WeisWyckoffSystem(df_renko)
ww.plot(iloc_value=30)Performing Full Analysis...
Calculating plots...
Completed! mpf.plot() was called, wait a moment!
ww.plot_wyckoff(iloc_value=30)Performing Wyckoff Analysis...
Calculating plots...
Completed! mpf.plot() was called, wait a moment!
from rangedf import Range
r = Range(df_ticks, range_size=5)
ww = WeisWyckoffSystem(r.range_df())
ww.plot_wyckoff(60, plot_time=False)Performing Wyckoff Analysis...
Calculating plots...
Completed! mpf.plot() was called, wait a moment!
