Python version of srl-ctrader-indicators
The features of each indicator are now fully synchronized with C# code.
The additional features developed here will be added (perhaps improved!) to C# version soon, at the proper time.
Currently, all indicators are plotted with 'plotly' library, but:
- multi_vwap can be plotted with mplfinance
- tpo_profile/volume_profile have limited support to plot with mplfinance (ex: no levels)
- weis_wyckoff_system no longer uses mplfinance.
See ‘notebooks’ folder for a detailed review of each indicator.
import pandas as pd
df_ticks = pd.read_parquet(f"data/US30_T1_2025_cT.parquet")
df_ticks.rename(columns={'bid': 'close'}, inplace=True)
df_ticks.head(3)
len(df_ticks)
df_ohlcv = pd.read_parquet(f"data/US30_1Minute_2025_cT.parquet")
df_ohlcv.head(3)
len(df_ohlcv)
from rangedf import Range
r = Range(df_ticks, range_size=14)
df_range = r.range_df()
render = 'png'| 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 |
180399
| 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 |
2631
from order_flow_aggregated import OrderFlowAggregated, SpikePlot
odft = OrderFlowAggregated(df_range, df_ticks, 3, is_open_time=False)
plot_params = SpikePlot(spike_chart=True, spike_levels=False)
odft.plot([50, 100], mode='delta', chart='ohlc', spike_plot=plot_params, renderer=render)
odft.plot_bubbles([350, 500], 'subtract', 'heatmap', renderer=render)
from volume_profile import VolumeProfile, DistributionData, ExtraProfile
vp = VolumeProfile(df_ohlcv, None, 7, pd.Timedelta(hours=12), DistributionData.OHLC_No_Avg)
vp.plot_ly('normal', nodes_source='lvn',
extra_profile=ExtraProfile.Mini, mini_interval=pd.Timedelta(hours=8),
renderer=render)
from tpo_profile import TpoProfile
tpo = TpoProfile(df_ohlcv, 7, pd.Timedelta(hours=8))
tpo.plot_ly(nodes_source='hvn', renderer=render)
from multi_vwap import MultiVwap, BandsFilter, BandsType
vwap = MultiVwap(df_ohlcv)
filter = BandsFilter(BandsType.Percentile_Asymmetric, volume_weighted=False)
vwap.plot_ly(bands_at='daily', bands_filter=filter, renderer=render)
from weis_wyckoff_system import WeisWyckoffSystem
from models_utils.ww_models import ZigZagInit, ZigZagMode, PriorityMode
ww = WeisWyckoffSystem()
_zz = ZigZagInit(ZigZagMode.Percentage, pct_value=0.02)
df = ww.full_analysis(df_ohlcv, None, None, zigzag_init=_zz)
df = df.iloc[10:150]
ww.plot(df, bar_time=False, bar_volume=False, turning_point=True, renderer=render)