pkg-support-resistance 0.4.1

Library collection of algorithms to calculate supports and resistance of financial markets.

PKG-SUPPORT-RESISTANCE

Description

Collection of algorithms to calculate supports and resistances in financial markets.

[!IMPORTANT]

Supported Algorithms

Supported Algorithms	Operational
Vanilla Clustering	✅
KMeans ML Clustering	✅
DBSCAN ML Clustering	✅
KNN (DK-Nearest Neighbors) ML	❌
RandomForest ML	❌

---

Installation

pip install pkg-support-resistance

Quick usage

Here's an example to get the gist of using the package.

from pkg_support_resistance import VanillaSupportResistance, KMeansSupportResistance, DBSCANSupportResistance
from pkg_support_resistance.data_set.data_extraction import sr_input_example

# Example input type
# sr_input_example = {
#     "open": [42780, 42834.94, ...],
#     "close": [42834.94, 42961.84, ...],
#     "high": [42901.1, 42986.06, ...],
#     "low": [42730.03, 42795.41, ...],
#     "volume": [173.63661, 169.47648, ...]
# }


# Vanilla algorithms:
sr_result: list[dict] = VanillaSupportResistance.exec_pipeline(input_data=sr_input_example, cluster_threshold=1)
print(sr_result, len(sr_result))


# KMeans ML algorithms:
sr_result: list[dict] = KMeansSupportResistance.exec_pipeline(input_data=sr_input_example, n_clusters=9)
print(sr_result, len(sr_result))


# DBSCAN  ML algorithms:
sr_result: list[dict] = DBSCANSupportResistance.exec_pipeline(input_data=sr_input_example, eps=1000, min_samples=1)
print(sr_result, len(sr_result))

Input example:

To see in: "/src/pkg_support_resistance/data_set/example.json"

Output example (Using VanillaSupportResistance algorithm and dataset from '/dataset/example.json'):

[
   {
      "pivotPrice": 48184.0,
      "limitsUp": 48495.0,
      "limitsDown": 47347.53,
      "score": 67,
      "accumulatedVolume": 44687.15635
   },
   {
      "pivotPrice": 47534.34,
      "limitsUp": 47874.98,
      "limitsDown": 46763.68,
      "score": 41,
      "accumulatedVolume": 23864.294279999995
   },
   {
      "pivotPrice": 45000.0,
      "limitsUp": 45000.0,
      "limitsDown": 44700.0,
      "score": 8,
      "accumulatedVolume": 6010.54166
   },
   {
      "pivotPrice": 43996.5,
      "limitsUp": 44141.37,
      "limitsDown": 43100.0,
      "score": 40,
      "accumulatedVolume": 17909.42211
   },
   {
      "pivotPrice": 43473.45,
      "limitsUp": 43580.0,
      "limitsDown": 42697.01,
      "score": 37,
      "accumulatedVolume": 18501.36496
   },
   {
      "pivotPrice": 42819.86,
      "limitsUp": 42850.0,
      "limitsDown": 42041.7,
      "score": 15,
      "accumulatedVolume": 6355.429050000001
   },
   {
      "pivotPrice": 42259.58,
      "limitsUp": 42787.38,
      "limitsDown": 42017.33,
      "score": 24,
      "accumulatedVolume": 12506.830009999998
   },
   {
      "pivotPrice": 41619.99,
      "limitsUp": 42365.48,
      "limitsDown": 41115.0,
      "score": 91,
      "accumulatedVolume": 42944.990900000004
   },
   {
      "pivotPrice": 41071.29,
      "limitsUp": 41157.26,
      "limitsDown": 40753.88,
      "score": 8,
      "accumulatedVolume": 4474.11918
   }
]

pivotPrice:

Support/resistance line.

limitsUp/limitsDown:

Support/resistance zone.

Score, support/resistance score:

If the score is high it means that many candles have been traded in that area with a high volume being traded, on the other hand a low score may be due to the fact that it is not a highly traded area or that it belongs to a higher maximum or lower minimum (very important zones, but not surpassed in the short term and low negotiation).

accumulatedVolume:

Accumulated volume traded in the consolidated zone between limitsUp/limitsDown.

Graph of the 'pivotPrice' S/R (Using VanillaSupportResistance algorithm and dataset from '/dataset/example.json'):