├── img
    ├── rsi_heatmap.png
    └── rsi_heatmap_reference.png
├── requirements.txt
├── LICENSE
├── README.md
├── .gitignore
└── src
    ├── data.py
    └── main.py


/img/rsi_heatmap.png:
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https://raw.githubusercontent.com/StephanAkkerman/crypto-rsi-heatmap/HEAD/img/rsi_heatmap.png


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/img/rsi_heatmap_reference.png:
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https://raw.githubusercontent.com/StephanAkkerman/crypto-rsi-heatmap/HEAD/img/rsi_heatmap_reference.png


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/requirements.txt:
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1 | numpy==1.24.4
2 | tqdm==4.65.0
3 | pycoingecko==3.1.0
4 | tradingview_ta==3.3.0
5 | matplotlib==3.9.0
6 | pandas==2.2.2
7 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2024 Stephan Akkerman
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | # Crypto Market RSI Heatmap 📊
 2 | 
 3 | This is a simple Python script that generates a RSI heatmap for the top 100 cryptocurrencies by volume using the style of [Coinglass](https://www.coinglass.com/pro/i/RsiHeatMap). The heatmap can be used to identify overbought and oversold conditions in the market.
 4 | 
 5 | <p align="center">
 6 |   <img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="Supported versions">
 7 |   <img src="https://img.shields.io/github/license/StephanAkkerman/liquidations-chart.svg?color=brightgreen" alt="License">
 8 |   <a href="https://github.com/psf/black"><img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="Code style: black"></a>
 9 | </p>
10 | 
11 | ---
12 | 
13 | ## Introduction
14 | 
15 | I have previously recreated this chart for my [fintwit-bot](https://github.com/StephanAkkerman/fintwit-bot), unfortunately coinglass removed their API so I had to recreate it using other sources. I used the chart found on [Coinglass](https://www.coinglass.com/pro/i/RsiHeatMap) as a reference. The data is fetched using [Coingecko's API](https://www.coingecko.com/en/api) to get the top volume coins and combined with [TradingView's API](https://github.com/AnalyzerREST/python-tradingview-ta) to get the RSI values.
16 | 
17 | ## Installation ⚙️
18 | 
19 | The required packages to run this code can be found in the requirements.txt file. To run this file, execute the following code block after cloning the repository:
20 | 
21 | ```bash
22 | pip install -r requirements.txt
23 | ```
24 | 
25 | ## Usage ⌨️
26 | 
27 | To generate the chart, simply run the script using the following command:
28 | 
29 | ```bash
30 | python src/main.py
31 | ```
32 | 
33 | ## Example 📊
34 | 
35 | The following chart is an example of the output generated by the script.
36 | ![RSI Heatmap](img/rsi_heatmap.png)
37 | 
38 | ### References 📚
39 | 
40 | The following image was used as a reference to create the RSI heatmap.
41 | ![RSI Heatmap Reference](img/rsi_heatmap_reference.png)
42 | 
43 | ## Other Projects 📦
44 | 
45 | This project is part of a series of projects that I have created. You can find the other projects in the following list:
46 | 
47 | - [Total Liquidation Chart](https://github.com/StephanAkkerman/liquidations-chart)
48 | - [Bitcoin Rainbow Chart](https://github.com/StephanAkkerman/bitcoin-rainbow-chart)
49 | - [Live Binance Charts](https://github.com/StephanAkkerman/live-binance-charts)
50 | 


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/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | data/
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | share/python-wheels/
 24 | *.egg-info/
 25 | .installed.cfg
 26 | *.egg
 27 | MANIFEST
 28 | 
 29 | # PyInstaller
 30 | #  Usually these files are written by a python script from a template
 31 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 32 | *.manifest
 33 | *.spec
 34 | 
 35 | # Installer logs
 36 | pip-log.txt
 37 | pip-delete-this-directory.txt
 38 | 
 39 | # Unit test / coverage reports
 40 | htmlcov/
 41 | .tox/
 42 | .nox/
 43 | .coverage
 44 | .coverage.*
 45 | .cache
 46 | nosetests.xml
 47 | coverage.xml
 48 | *.cover
 49 | *.py,cover
 50 | .hypothesis/
 51 | .pytest_cache/
 52 | cover/
 53 | 
 54 | # Translations
 55 | *.mo
 56 | *.pot
 57 | 
 58 | # Django stuff:
 59 | *.log
 60 | local_settings.py
 61 | db.sqlite3
 62 | db.sqlite3-journal
 63 | 
 64 | # Flask stuff:
 65 | instance/
 66 | .webassets-cache
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | .pybuilder/
 76 | target/
 77 | 
 78 | # Jupyter Notebook
 79 | .ipynb_checkpoints
 80 | 
 81 | # IPython
 82 | profile_default/
 83 | ipython_config.py
 84 | 
 85 | # pyenv
 86 | #   For a library or package, you might want to ignore these files since the code is
 87 | #   intended to run in multiple environments; otherwise, check them in:
 88 | # .python-version
 89 | 
 90 | # pipenv
 91 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 92 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 93 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 94 | #   install all needed dependencies.
 95 | #Pipfile.lock
 96 | 
 97 | # poetry
 98 | #   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
 99 | #   This is especially recommended for binary packages to ensure reproducibility, and is more
100 | #   commonly ignored for libraries.
101 | #   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
102 | #poetry.lock
103 | 
104 | # pdm
105 | #   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
106 | #pdm.lock
107 | #   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
108 | #   in version control.
109 | #   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
110 | .pdm.toml
111 | .pdm-python
112 | .pdm-build/
113 | 
114 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
115 | __pypackages__/
116 | 
117 | # Celery stuff
118 | celerybeat-schedule
119 | celerybeat.pid
120 | 
121 | # SageMath parsed files
122 | *.sage.py
123 | 
124 | # Environments
125 | .env
126 | .venv
127 | env/
128 | venv/
129 | ENV/
130 | env.bak/
131 | venv.bak/
132 | 
133 | # Spyder project settings
134 | .spyderproject
135 | .spyproject
136 | 
137 | # Rope project settings
138 | .ropeproject
139 | 
140 | # mkdocs documentation
141 | /site
142 | 
143 | # mypy
144 | .mypy_cache/
145 | .dmypy.json
146 | dmypy.json
147 | 
148 | # Pyre type checker
149 | .pyre/
150 | 
151 | # pytype static type analyzer
152 | .pytype/
153 | 
154 | # Cython debug symbols
155 | cython_debug/
156 | 
157 | # PyCharm
158 | #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
159 | #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
160 | #  and can be added to the global gitignore or merged into this file.  For a more nuclear
161 | #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
162 | #.idea/
163 | 


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/src/data.py:
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  1 | import os
  2 | import pickle
  3 | import time
  4 | from datetime import datetime, timedelta
  5 | 
  6 | import pandas as pd
  7 | from pycoingecko import CoinGeckoAPI
  8 | from tradingview_ta import get_multiple_analysis
  9 | 
 10 | cg = CoinGeckoAPI()
 11 | 
 12 | 
 13 | def get_RSI(coins: list, exchange: str = "BINANCE", time_frame: str = "1d") -> dict:
 14 |     # Format symbols exchange:symbol
 15 |     symbols = [f"{exchange.upper()}:{symbol}" for symbol in coins]
 16 | 
 17 |     analysis = get_multiple_analysis(
 18 |         symbols=symbols, interval=time_frame, screener="crypto"
 19 |     )
 20 | 
 21 |     # For each symbol get the RSI
 22 |     rsi_dict = {}
 23 |     for symbol in symbols:
 24 |         if analysis[symbol] is None:
 25 |             # print(f"No analysis for {symbol}")
 26 |             continue
 27 |         clean_symbol = symbol.replace(f"{exchange.upper()}:", "")
 28 |         clean_symbol = clean_symbol.replace("USDT", "")
 29 |         rsi_dict[clean_symbol] = analysis[symbol].indicators["RSI"]
 30 | 
 31 |     # Save the RSI data to a CSV file
 32 |     save_RSI(rsi_dict, time_frame)
 33 | 
 34 |     return rsi_dict
 35 | 
 36 | 
 37 | def get_closest_to_24h(
 38 |     file_path: str = "data/rsi_data.csv", time_frame: str = "1d"
 39 | ) -> dict:
 40 |     # Read the CSV file into a DataFrame
 41 |     if not os.path.isfile(file_path):
 42 |         print(f"No data found in {file_path}")
 43 |         return {}
 44 | 
 45 |     df = pd.read_csv(file_path)
 46 | 
 47 |     # Filter on the timeframe
 48 |     df = df[df["Time Frame"] == time_frame]
 49 | 
 50 |     # Convert the 'Date' column to datetime
 51 |     df["Date"] = pd.to_datetime(df["Date"])
 52 | 
 53 |     # Calculate the time difference from 24 hours ago
 54 |     target_time = datetime.now() - timedelta(hours=24)
 55 |     df["Time_Diff"] = abs(df["Date"] - target_time)
 56 | 
 57 |     # Find the minimum time difference
 58 |     min_time_diff = df["Time_Diff"].min()
 59 | 
 60 |     # Filter rows that have the minimum time difference
 61 |     closest_rows = df[df["Time_Diff"] == min_time_diff]
 62 | 
 63 |     # Convert the filtered rows to a dictionary with symbols as keys and RSI as values
 64 |     result = closest_rows.set_index("Symbol")["RSI"].to_dict()
 65 | 
 66 |     return result
 67 | 
 68 | 
 69 | def save_RSI(
 70 |     rsi_dict: dict, time_frame: str, file_path: str = "data/rsi_data.csv"
 71 | ) -> None:
 72 |     # Convert the RSI dictionary to a DataFrame
 73 |     df = pd.DataFrame(list(rsi_dict.items()), columns=["Symbol", "RSI"])
 74 | 
 75 |     # Add the current date to the DataFrame
 76 |     df["Date"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 77 |     df["Time Frame"] = time_frame
 78 | 
 79 |     # Check if the file exists
 80 |     if os.path.isfile(file_path):
 81 |         # Append data to the existing CSV file
 82 |         df.to_csv(file_path, mode="a", header=False, index=False)
 83 |     else:
 84 |         # Save the DataFrame to a new CSV file with header
 85 |         df.to_csv(file_path, index=False)
 86 | 
 87 |     print(f"RSI data saved to {file_path}")
 88 | 
 89 | 
 90 | def get_top_vol_coins(length: int = 100) -> list:
 91 | 
 92 |     CACHE_FILE = "data/top_vol_coins_cache.pkl"
 93 |     CACHE_EXPIRATION = 24 * 60 * 60  # 24 hours in seconds
 94 |     # List of symbols to exclude
 95 |     STABLE_COINS = [
 96 |         "OKBUSDT",
 97 |         "DAIUSDT",
 98 |         "USDTUSDT",
 99 |         "USDCUSDT",
100 |         "BUSDUSDT",
101 |         "TUSDUSDT",
102 |         "PAXUSDT",
103 |         "EURUSDT",
104 |         "GBPUSDT",
105 |         "CETHUSDT",
106 |         "WBTCUSDT",
107 |     ]
108 | 
109 |     # Check if the cache file exists and is not expired
110 |     os.makedirs(CACHE_FILE.split("/")[0], exist_ok=True)
111 |     if os.path.exists(CACHE_FILE):
112 |         with open(CACHE_FILE, "rb") as f:
113 |             cache_data = pickle.load(f)
114 |             cache_time = cache_data["timestamp"]
115 |             if time.time() - cache_time < CACHE_EXPIRATION:
116 |                 # Return the cached data if it's not expired
117 |                 print("Using cached top volume coins")
118 |                 return cache_data["data"][:length]
119 | 
120 |     # Fetch fresh data if the cache is missing or expired
121 |     df = pd.DataFrame(cg.get_coins_markets("usd"))["symbol"].str.upper() + "USDT"
122 | 
123 |     sorted_volume = df[~df.isin(STABLE_COINS)]
124 |     top_vol_coins = sorted_volume.tolist()
125 | 
126 |     # Save the result to the cache
127 |     with open(CACHE_FILE, "wb") as f:
128 |         pickle.dump({"timestamp": time.time(), "data": top_vol_coins}, f)
129 | 
130 |     return top_vol_coins[:length]
131 | 


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/src/main.py:
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  1 | import matplotlib.pyplot as plt
  2 | import numpy as np
  3 | 
  4 | from data import get_closest_to_24h, get_RSI, get_top_vol_coins
  5 | 
  6 | FIGURE_SIZE = (12, 10)
  7 | BACKGROUND_COLOR = "#0d1117"
  8 | RANGES = {
  9 |     "Overbought": (70, 100),
 10 |     "Strong": (60, 70),
 11 |     "Neutral": (40, 60),
 12 |     "Weak": (30, 40),
 13 |     "Oversold": (0, 30),
 14 | }
 15 | COLORS_LABELS = {
 16 |     "Oversold": "#1d8b7a",
 17 |     "Weak": "#144e48",
 18 |     "Neutral": "#0d1117",
 19 |     "Strong": "#681f28",
 20 |     "Overbought": "#c32e3b",
 21 | }
 22 | SCATTER_COLORS = {
 23 |     "Oversold": "#1e9884",
 24 |     "Weak": "#165952",
 25 |     "Neutral": "#78797a",
 26 |     "Strong": "#79212c",
 27 |     "Overbought": "#cf2f3d",
 28 | }
 29 | 
 30 | 
 31 | def get_color_for_rsi(rsi_value: float) -> dict:
 32 |     for label, (low, high) in RANGES.items():
 33 |         if low <= rsi_value < high:
 34 |             return SCATTER_COLORS[label]
 35 |     return None
 36 | 
 37 | 
 38 | def plot_rsi_heatmap(num_coins: int = 100, time_frame: str = "1d") -> None:
 39 |     top_vol = get_top_vol_coins(num_coins)
 40 |     rsi_data = get_RSI(top_vol, time_frame=time_frame)
 41 |     old_rsi_data = get_closest_to_24h(time_frame=time_frame)
 42 | 
 43 |     # Create lists of labels and RSI values
 44 |     rsi_symbols = list(rsi_data.keys())
 45 |     rsi_values = list(rsi_data.values())
 46 | 
 47 |     # Calculate the average RSI value
 48 |     average_rsi = np.mean(rsi_values)
 49 | 
 50 |     # Create the scatter plot
 51 |     fig, ax = plt.subplots(figsize=FIGURE_SIZE)
 52 | 
 53 |     # Set the background color
 54 |     fig.patch.set_facecolor(BACKGROUND_COLOR)
 55 |     ax.set_facecolor(BACKGROUND_COLOR)
 56 | 
 57 |     # Define the color for each RSI range
 58 |     color_map = []
 59 |     for k in RANGES:
 60 |         color_map.append((*RANGES[k], COLORS_LABELS[k], k))
 61 | 
 62 |     # Fill the areas with the specified colors and create custom legend
 63 |     for i, (start, end, color, symbol) in enumerate(color_map):
 64 |         ax.fill_between([0, len(rsi_symbols) + 2], start, end, color=color, alpha=0.35)
 65 | 
 66 |         # Adjust the Y position for the first and last labels
 67 |         if i == 0:
 68 |             y_pos = start + 5  # Move down a bit from the top
 69 |         elif i == len(color_map) - 1:
 70 |             y_pos = end - 5  # Move up a bit from the bottom
 71 |         else:
 72 |             y_pos = (start + end) / 2  # Center for other labels
 73 | 
 74 |         # Add text to the right of the plot with the label (overbought, etc.)
 75 |         ax.text(
 76 |             len(rsi_symbols) + 1.5,  # X position (to the right of the plot)
 77 |             y_pos,  # Y position
 78 |             symbol.upper(),  # Text to display
 79 |             va="center",  # Vertical alignment
 80 |             ha="right",  # Horizontal alignment
 81 |             fontsize=15,  # Font size
 82 |             color="grey",  # Text color
 83 |         )
 84 | 
 85 |     # Plot each point with a white border for visibility
 86 |     for i, symbol in enumerate(rsi_symbols):
 87 |         # These are the dots on the plot
 88 |         ax.scatter(
 89 |             i + 1,
 90 |             rsi_values[i],
 91 |             color=get_color_for_rsi(rsi_values[i]),
 92 |             s=100,
 93 |         )
 94 |         # Add the symbol text
 95 |         ax.annotate(
 96 |             symbol,
 97 |             (i + 1, rsi_values[i]),
 98 |             color="#b9babc",
 99 |             textcoords="offset points",
100 |             xytext=(0, 10),
101 |             ha="center",
102 |         )
103 |         # Add line connecting the old and new RSI values
104 |         if symbol in old_rsi_data:
105 |             # Compare the previous RSI value with the current one
106 |             rsi_diff = rsi_values[i] - old_rsi_data[symbol]
107 | 
108 |             # Set the color based on the difference
109 |             line_color = "#1f9986" if rsi_diff > 0 else "#e23343"
110 | 
111 |             # Draw the line connecting the old and new RSI values
112 |             ax.plot(
113 |                 [i + 1, i + 1],
114 |                 [old_rsi_data[symbol], rsi_values[i]],
115 |                 color=line_color,
116 |                 linestyle="--",
117 |                 linewidth=0.75,  # Adjust the value to make the lines thinner
118 |             )
119 | 
120 |     # Draw the average RSI line and add the annotation
121 |     ax.axhline(
122 |         xmin=0, xmax=1, y=average_rsi, color="#d58c3c", linestyle="--", linewidth=0.75
123 |     )
124 |     ax.text(
125 |         len(rsi_symbols) + 1.5,  # Increase to move the text to the right
126 |         average_rsi,
127 |         f"AVG RSI: {average_rsi:.2f}",
128 |         color="#d58c3c",
129 |         va="bottom",
130 |         ha="right",
131 |         fontsize=15,
132 |     )
133 | 
134 |     # Set the color of the tick labels to white
135 |     ax.tick_params(colors="#a9aaab", which="both", length=0)
136 | 
137 |     # Set the y-axis limits based on RSI values
138 |     ax.set_ylim(20, 80)
139 | 
140 |     # Extend the xlim to make room for the annotations
141 |     ax.set_xlim(0, len(rsi_symbols) + 2)
142 | 
143 |     # Remove the x-axis ticks since we're annotating each point
144 |     ax.set_xticks([])
145 | 
146 |     add_legend(ax)
147 | 
148 |     # Set the color of the spines to match the background color or make them invisible
149 |     for spine in ax.spines.values():
150 |         spine.set_edgecolor(BACKGROUND_COLOR)
151 | 
152 |     # Add the title in the top left corner
153 |     plt.text(
154 |         -0.025,
155 |         1.125,
156 |         "Crypto Market RSI Heatmap",
157 |         transform=ax.transAxes,
158 |         fontsize=14,
159 |         verticalalignment="top",
160 |         horizontalalignment="left",
161 |         color="white",
162 |         weight="bold",
163 |     )
164 | 
165 |     plt.show()
166 | 
167 | 
168 | def add_legend(ax: plt.Axes) -> None:
169 |     # Create custom legend handles with square markers, including BTC price
170 |     adjusted_colors = list(COLORS_LABELS.values())
171 |     # Change NEUTRAL color to grey
172 |     adjusted_colors[2] = "#808080"
173 |     legend_handles = [
174 |         plt.Line2D(
175 |             [0],
176 |             [0],
177 |             marker="s",
178 |             color=BACKGROUND_COLOR,
179 |             markerfacecolor=color,
180 |             markersize=10,
181 |             label=label,
182 |         )
183 |         for color, label in zip(
184 |             adjusted_colors,
185 |             [label.upper() for label in list(COLORS_LABELS.keys())],
186 |         )
187 |     ]
188 | 
189 |     # Add legend
190 |     legend = ax.legend(
191 |         handles=legend_handles,
192 |         loc="upper center",
193 |         bbox_to_anchor=(0.5, 1.05),
194 |         ncol=len(legend_handles),
195 |         frameon=False,
196 |         fontsize="small",
197 |         labelcolor="white",
198 |     )
199 | 
200 |     # Make legend text bold
201 |     for text in legend.get_texts():
202 |         text.set_fontweight("bold")
203 | 
204 |     # Adjust layout to reduce empty space around the plot
205 |     plt.subplots_adjust(left=0.05, right=0.95, top=0.875, bottom=0.1)
206 | 
207 | 
208 | if __name__ == "__main__":
209 |     plot_rsi_heatmap(num_coins=100, time_frame="1d")
210 | 


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