price api overhaul

algorithms/bollinger_bands.py

@@ -1,12 +1,13 @@
 from talib import BBANDS
 from price import get_periods
 
-def algorithm(prices, window_size=get_periods(20, 'days'), standard_deviations=2):
-	upper_band, middle_band, lower_band = BBANDS(prices, window_size, standard_deviations, standard_deviations, 0)
-	return upper_band, lower_band, middle_band
+def algorithm(prices, window_size=(20, 'days'), standard_deviations=2):
+	print(prices.shape[0])
+	print(get_periods(*window_size))
+	return BBANDS(prices, timeperiod=get_periods(*window_size), nbdevup=standard_deviations, nbdevdn=standard_deviations)
 
 def signal(prices, data):
-	upper_band, lower_band, middle_band = data
+	upper_band, middle_band, lower_band = data
 	if prices[-1] > upper_band[-1]:
 		return 'sell', 1
 	elif prices[-1] < lower_band[-1]:

algorithms/custom_bollinger_rsi.py

@@ -1,12 +1,11 @@
-from price import get_periods
 from .bollinger_bands import algorithm as bollinger_bands
 from .rsi import algorithm as rsi
 
-def algorithm(prices, window_size_rsi=get_periods(13, 'days'), window_size_bollinger_bands=get_periods(1, 'month')):
-	upper_band, lower_band, middle_band = bollinger_bands(prices, window_size=window_size_bollinger_bands)
+def algorithm(prices, window_size_rsi=(13, 'days'), window_size_bollinger_bands=(1, 'month')):
+	bb_data = bollinger_bands(prices, window_size=window_size_bollinger_bands)
 	rsi_line = rsi(prices, window_size=window_size_rsi)
 
-	return upper_band, lower_band, middle_band, rsi_line
+	return [*bb_data, rsi_line]
 
 def signal(prices, data):
 	price = prices[-1]

algorithms/rsi.py

@@ -1,8 +1,8 @@
 from talib import RSI
 from price import get_periods
 
-def algorithm(prices, window_size=get_periods(14, 'days')):
-	return RSI(prices, window_size)
+def algorithm(prices, window_size=(14, 'days')):
+	return RSI(prices, timeperiod=get_periods(*window_size))
 
 def signal(prices, data, high=70, low=30):
 	rsi = data

environment.yml

@@ -3,61 +3,123 @@ channels:
   - conda-forge
   - defaults
 dependencies:
+  - _libgcc_mutex=0.1
+  - _openmp_mutex=4.5
+  - binutils_linux-64=2.28.1
+  - blas=1.1
+  - brotli=1.0.9
+  - brotli-bin=1.0.9
   - bzip2=1.0.8
-  - ca-certificates=2023.7.22
-  - intel-openmp=2023.2.0
-  - libblas=3.9.0
-  - libcblas=3.9.0
-  - libffi=3.4.4
-  - liblapack=3.9.0
+  - ca-certificates=2023.08.22
+  - contourpy=1.0.5
+  - cycler=0.11.0
+  - cyrus-sasl=2.1.28
+  - dbus=1.13.18
+  - expat=2.5.0
+  - fontconfig=2.14.1
+  - fonttools=4.25.0
+  - freetype=2.12.1
+  - gcc_linux-64=7.2.0
+  - giflib=5.2.1
+  - glib=2.69.1
+  - gst-plugins-base=1.14.1
+  - gstreamer=1.14.1
+  - icu=58.2
+  - jpeg=9e
+  - kiwisolver=1.4.4
+  - krb5=1.20.1
+  - lcms2=2.12
+  - ld_impl_linux-64=2.38
+  - lerc=3.0
+  - libbrotlicommon=1.0.9
+  - libbrotlidec=1.0.9
+  - libbrotlienc=1.0.9
+  - libclang=14.0.6
+  - libclang13=14.0.6
+  - libcups=2.4.2
+  - libdeflate=1.17
+  - libedit=3.1.20221030
+  - libevent=2.1.12
+  - libffi=3.3
+  - libgcc-ng=13.2.0
+  - libgfortran-ng=13.2.0
+  - libgfortran5=13.2.0
+  - libllvm14=14.0.6
+  - libopenblas=0.3.24
+  - libpng=1.6.39
+  - libpq=12.15
+  - libstdcxx-ng=11.2.0
   - libta-lib=0.4.0
-  - mkl=2020.4
-  - openssl=3.0.11
+  - libtiff=4.5.1
+  - libuuid=1.41.5
+  - libwebp=1.3.2
+  - libwebp-base=1.3.2
+  - libxcb=1.15
+  - libxkbcommon=1.0.1
+  - libxml2=2.10.4
+  - libxslt=1.1.37
+  - llvm-openmp=14.0.6
+  - lz4-c=1.9.4
+  - matplotlib=3.7.2
+  - matplotlib-base=3.7.2
+  - munkres=1.1.4
+  - mysql=5.7.24
+  - ncurses=6.4
+  - nspr=4.35
+  - nss=3.89.1
+  - numpy=1.26.0
+  - numpy-base=1.26.0
+  - openblas=0.3.24
+  - openjpeg=2.4.0
+  - openssl=1.1.1w
+  - packaging=23.1
+  - pcre=8.45
+  - pillow=10.0.1
   - pip=23.2.1
-  - python=3.10.13
+  - ply=3.11
+  - pyparsing=3.0.9
+  - pyqt=5.15.7
+  - python=3.10.0
+  - python-dateutil=2.8.2
   - python_abi=3.10
+  - qt-main=5.15.2
+  - qt-webengine=5.15.9
+  - qtwebkit=5.212
+  - readline=8.2
   - setuptools=68.0.0
+  - sip=6.6.2
+  - six=1.16.0
   - sqlite=3.41.2
-  - ta-lib=0.4.19
+  - ta-lib=0.4.28
   - tk=8.6.12
-  - vc=14.2
-  - vs2015_runtime=14.27.29016
+  - toml=0.10.2
+  - tornado=6.3.3
+  - tzdata=2023c
   - wheel=0.41.2
   - xz=5.4.2
   - zlib=1.2.13
+  - zstd=1.5.5
   - pip:
-      - async-timeout==4.0.3
-      - blinker==1.6.2
+      - backtrader==1.9.78.123
+      - blinker==1.6.3
       - certifi==2023.7.22
-      - charset-normalizer==3.3.0
-      - click==8.1.7
-      - colorama==0.4.6
-      - contourpy==1.1.1
-      - cycler==0.12.0
       - dnspython==2.4.2
-      - flask==2.3.3
-      - fonttools==4.43.0
+      - flask==3.0.0
       - idna==3.4
       - itsdangerous==2.1.2
       - jinja2==3.1.2
-      - kiwisolver==1.4.5
       - markupsafe==2.1.3
-      - matplotlib==3.8.0
-      - numpy==1.26.0
-      - packaging==23.1
-      - pandas==2.1.1
-      - pillow==10.0.1
+      - platformdirs==3.11.0
       - pyjwt==2.8.0
       - pymongo==4.5.0
-      - pyparsing==3.1.1
-      - python-dateutil==2.8.2
+      - pyqt5-sip==12.11.0
       - python-dotenv==1.0.0
-      - pytz==2023.3.post1
-      - redis==5.0.0
       - requests==2.31.0
       - schedule==1.2.1
-      - six==1.16.0
-      - tzdata==2023.3
-      - urllib3==2.0.5
-      - werkzeug==2.3.7
-prefix: C:\Users\Celestial2\miniconda3\envs\auto-trading
+      - tomli==2.0.1
+      - urllib3==2.0.6
+      - waitress==2.1.2
+      - werkzeug==3.0.0
+      - yapf==0.40.2
+      - zipp==3.17.0
+prefix: /home/celestial/anaconda3/envs/auto-trading

plots/bollinger_bands.py

@@ -3,23 +3,20 @@
 from algorithms.bollinger_bands import algorithm as boillinger_bands
 import plots.colors as colors
 
-def plot(prices, **kwargs):
-	upper_band, lower_band, middle_band = boillinger_bands(prices, **kwargs)
+def plot(prices, timestamps, **kwargs):
+	upper_band, middle_band, lower_band = boillinger_bands(prices, window_size=(20, 'days'), **kwargs)
 
-	indicies = np.arange(0, upper_band.shape[0])
-	sliced_prices = prices[:upper_band.shape[0]]
-
-	plt.fill_between(indicies, upper_band, lower_band, color='grey', alpha=0.3)
+	plt.fill_between(timestamps, upper_band, lower_band, color='grey', alpha=0.3)
 
 	# Price/SMA
-	plt.plot(indicies, sliced_prices, color=colors.mainline())
-	plt.plot(indicies, middle_band, color=colors.secondaryline())
+	plt.plot(timestamps, prices, color=colors.mainline())
+	plt.plot(timestamps, middle_band, color=colors.secondaryline())
 
 	# Buy/Sell Signals
-	upper_condition = sliced_prices >= upper_band
-	lower_condition = sliced_prices <= lower_band
+	upper_condition = prices >= upper_band
+	lower_condition = prices <= lower_band
 
-	plt.scatter(indicies[upper_condition], sliced_prices[upper_condition], color=colors.upper())
-	plt.scatter(indicies[lower_condition], sliced_prices[lower_condition], color=colors.lower())
+	plt.scatter(timestamps[upper_condition], prices[upper_condition], color=colors.upper())
+	plt.scatter(timestamps[lower_condition], prices[lower_condition], color=colors.lower())
 
 	return plt

plots/custom_bollinger_rsi.py

@@ -1,30 +1,27 @@
-import numpy as np
 import matplotlib.pyplot as plt
 import plots.colors as colors
 from matplotlib.gridspec import GridSpec
 from algorithms.custom_bollinger_rsi import algorithm as custom_bollinger_rsi
 from plots.rsi import plot as rsi
 from plots.bollinger_bands import plot as bollinger_bands
 
-def plot(prices, **kwargs):
+def plot(prices, timestamps, **kwargs):
 	gs = GridSpec(3, 1, figure=plt.gcf())
 
 	plt.subplot(gs[0, :])
-	indicies = np.arange(0, prices.shape[0])
-	plt.plot(indicies, prices, color=colors.mainline())
+	plt.plot(timestamps, prices, color=colors.mainline())
 
-	upper_band, lower_band, middle_band, rsi_line = custom_bollinger_rsi(prices, **kwargs)
+	upper_band, middle_band, lower_band, rsi_line = custom_bollinger_rsi(prices, **kwargs)
 	sliced_prices = prices[:min(upper_band.shape[0], rsi_line.shape[0])]
-	indicies = np.arange(0, indicies.shape[0])
 
 	upper_condition = (prices >= upper_band) & (rsi_line >= 70)
 	lower_condition = (prices >= lower_band) & (30 >= rsi_line)
-	plt.scatter(indicies[upper_condition], sliced_prices[upper_condition], color=colors.upper())
-	plt.scatter(indicies[lower_condition], sliced_prices[lower_condition], color=colors.lower())
+	plt.scatter(timestamps[upper_condition], sliced_prices[upper_condition], color=colors.upper())
+	plt.scatter(timestamps[lower_condition], sliced_prices[lower_condition], color=colors.lower())
 
 	plt.subplot(gs[-1, :])
-	rsi(prices)
+	rsi(prices, timestamps)
 	plt.subplot(gs[-2, :])
-	bollinger_bands(prices)
+	bollinger_bands(prices, timestamps)
 
 	return plt

plots/price.py

@@ -2,8 +2,7 @@
 import matplotlib.pyplot as plt
 import plots.colors as colors
 
-def plot(prices):
-	indicies = np.arange(0, prices.shape[0])
-	plt.plot(indicies, prices, color=colors.mainline())
+def plot(prices, timestamps):
+	plt.plot(timestamps, prices, color=colors.mainline())
 
 	return plt

plots/rsi.py

@@ -1,20 +1,19 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from algorithms.rsi import algorithm as rsi
 import plots.colors as colors
+from algorithms.rsi import algorithm as rsi
 
-def plot(prices, **kwargs):
+def plot(prices, timestamps, **kwargs):
 	rsi_line = rsi(prices, **kwargs)
-	indicies = np.arange(0, rsi_line.shape[0])
 
-	plt.plot(indicies, rsi_line, color=colors.mainline())
+	plt.plot(timestamps, rsi_line, color=colors.mainline())
 
 	# Thresholds
 	upper = np.full(rsi_line.shape, 70)
 	lower = np.full(rsi_line.shape, 30)
 
-	plt.fill_between(indicies, upper, lower, color='grey', alpha=0.3)
-	plt.plot(indicies, upper, linestyle='dashed', color=colors.upper())
-	plt.plot(indicies, lower, linestyle='dashed', color=colors.lower())
+	plt.fill_between(timestamps, upper, lower, color='grey', alpha=0.3)
+	plt.plot(timestamps, upper, linestyle='dashed', color=colors.upper())
+	plt.plot(timestamps, lower, linestyle='dashed', color=colors.lower())
 
 	return plt

plots/worth.py

@@ -1,12 +1,11 @@
 import matplotlib.pyplot as plt
 import plots.colors as colors
-import datetime as dt
 import numpy as np
+from datetime import datetime as dt
 
-def plot(timestamps, values):
+def plot(values, timestamps):
 	values = np.array(values)
-	# times = [ dt.datetime.fromtimestamp(timestamp) for timestamp in timestamps ]
-	times = np.arange(0, values.shape[0]) * 5
-	plt.plot(times, values, color=colors.upper())
+	timestamps = [dt.fromtimestamp(timestamp) for timestamp in timestamps]
+	plt.plot(timestamps, values, color=colors.upper())
 
 	return plt