-pybottrader
- -PyBotTrader - A library to build trader bots
- -To install:
- -pip install git+https://github.com/jailop/pybottrader.git
-To install:
- -pip install git+https://github.com/jailop/pybottrader.git
-Data Streamers
-1"""Data Streamers""" - 2 - 3from typing import Union - 4import pandas as pd - 5import yfinance - 6 - 7 - 8class DataStreamer: - 9 """A data streamer abstract class""" -10 -11 def __init__(self): -12 """Init method""" -13 -14 def next(self) -> Union[dict, None]: -15 """Next method""" -16 -17 -18class YFinanceStreamer(DataStreamer): -19 """Using Yahoo Finance to retrieve data""" -20 -21 index = 0 -22 data: pd.DataFrame -23 -24 def __init__(self, symbol, *args, **kwargs): -25 super().__init__() -26 ticker = yfinance.Ticker(symbol) -27 self.data = ticker.history(*args, **kwargs) -28 self.data.rename( -29 columns={ -30 "Open": "open", -31 "Close": "close", -32 "High": "high", -33 "Low": "low", -34 "Volume": "volume", -35 }, -36 inplace=True, -37 ) -38 self.data.index.names = ["time"] -39 self.data.reset_index(inplace=True) -40 -41 def next(self) -> Union[dict, None]: -42 if self.index >= len(self.data): -43 return None -44 result = self.data.iloc[self.index].to_dict() -45 result["time"] = result["time"].to_pydatetime() -46 self.index += 1 -47 return result -48 -49 -50class CSVFileStreamer(DataStreamer): -51 """ -52 An dataframe file streamer -53 """ -54 -55 data: pd.DataFrame -56 index: int -57 -58 def __init__(self, filename: str): -59 self.index = 0 -60 self.data = pd.read_csv(filename, parse_dates=True) -61 -62 def next(self) -> Union[dict, None]: -63 if self.index >= len(self.data): -64 return None -65 result = self.data.iloc[self.index].to_dict() -66 self.index += 1 -67 return result -
9class DataStreamer: -10 """A data streamer abstract class""" -11 -12 def __init__(self): -13 """Init method""" -14 -15 def next(self) -> Union[dict, None]: -16 """Next method""" -
A data streamer abstract class
-19class YFinanceStreamer(DataStreamer): -20 """Using Yahoo Finance to retrieve data""" -21 -22 index = 0 -23 data: pd.DataFrame -24 -25 def __init__(self, symbol, *args, **kwargs): -26 super().__init__() -27 ticker = yfinance.Ticker(symbol) -28 self.data = ticker.history(*args, **kwargs) -29 self.data.rename( -30 columns={ -31 "Open": "open", -32 "Close": "close", -33 "High": "high", -34 "Low": "low", -35 "Volume": "volume", -36 }, -37 inplace=True, -38 ) -39 self.data.index.names = ["time"] -40 self.data.reset_index(inplace=True) -41 -42 def next(self) -> Union[dict, None]: -43 if self.index >= len(self.data): -44 return None -45 result = self.data.iloc[self.index].to_dict() -46 result["time"] = result["time"].to_pydatetime() -47 self.index += 1 -48 return result -
Using Yahoo Finance to retrieve data
-25 def __init__(self, symbol, *args, **kwargs): -26 super().__init__() -27 ticker = yfinance.Ticker(symbol) -28 self.data = ticker.history(*args, **kwargs) -29 self.data.rename( -30 columns={ -31 "Open": "open", -32 "Close": "close", -33 "High": "high", -34 "Low": "low", -35 "Volume": "volume", -36 }, -37 inplace=True, -38 ) -39 self.data.index.names = ["time"] -40 self.data.reset_index(inplace=True) -
Init method
-42 def next(self) -> Union[dict, None]: -43 if self.index >= len(self.data): -44 return None -45 result = self.data.iloc[self.index].to_dict() -46 result["time"] = result["time"].to_pydatetime() -47 self.index += 1 -48 return result -
Next method
-51class CSVFileStreamer(DataStreamer): -52 """ -53 An dataframe file streamer -54 """ -55 -56 data: pd.DataFrame -57 index: int -58 -59 def __init__(self, filename: str): -60 self.index = 0 -61 self.data = pd.read_csv(filename, parse_dates=True) -62 -63 def next(self) -> Union[dict, None]: -64 if self.index >= len(self.data): -65 return None -66 result = self.data.iloc[self.index].to_dict() -67 self.index += 1 -68 return result -
An dataframe file streamer
-Moving Average -Implementation adopted from: -https://github.com/jailop/trading/tree/main/indicators-c++
-1""" - 2Moving Average - 3Implementation adopted from: - 4https://github.com/jailop/trading/tree/main/indicators-c++ - 5""" - 6 - 7import numpy as np - 8from attrs import define - 9 - 10 - 11class Indicator: - 12 """ - 13 Base class to build indicators. It provides a buffer to - 14 keep the last `mem_size` values and functions to push - 15 new values and retrieve using bracket notation or the - 16 function get. - 17 - 18 All derived classes, call the __init__ method of this class - 19 and when an update ocurrs, they push the new value - 20 into the memory buffer. - 21 - 22 Because this class is intended to be used for time series, - 23 indices go backwards, being `0` the last updated value, -1 - 24 the previous one, -2 the previous of the previos one... - 25 If a value is requested with a positive index or a negative - 26 index which absolute values is greater than `mem_size`, then - 27 an NAN value is returned. - 28 """ - 29 - 30 mem_pos: int - 31 mem_data: list - 32 mem_size: int - 33 - 34 def __init__(self, mem_size=1): - 35 """ - 36 @param mem_size: The size of the memory buffer. The - 37 default value is 1. - 38 """ - 39 self.mem_data = [np.nan] * mem_size - 40 self.mem_pos = 0 - 41 self.mem_size = mem_size - 42 - 43 def __getitem__(self, key): - 44 """ - 45 @param key: 0 for the most recent update - 46 a negative number for the n-previous updates - 47 - 48 A negative number less than -mem_size returns NAN. - 49 A positive number returns NAN. - 50 """ - 51 if key > 0 or -key >= self.mem_size: - 52 return np.nan - 53 real_pos = (self.mem_pos - key) % self.mem_size - 54 return self.mem_data[real_pos] - 55 - 56 def push(self, value): - 57 """ - 58 Stores in the buffer `value` as the more recent update. - 59 In the current implementation, a ring buffer is used - 60 to save these values. - 61 - 62 @param value: The most recent update - 63 """ - 64 self.mem_pos = (self.mem_pos - 1) % self.mem_size - 65 self.mem_data[self.mem_pos] = value - 66 - 67 def get(self, key=0) -> float: - 68 """The same as `__getitem__`""" - 69 return self[key] - 70 - 71 - 72class MA(Indicator): - 73 """Moving Average""" - 74 - 75 period: int - 76 prevs: np.ndarray - 77 length: int = 0 - 78 pos: int = 0 - 79 accum: float = 0.0 - 80 - 81 def __init__(self, period: int, *args, **kwargs): - 82 """ - 83 The number of period or window size is required to initialize a MA - 84 object. - 85 """ - 86 super().__init__(*args, **kwargs) - 87 self.period = period - 88 self.prevs = np.zeros(period, dtype=float) - 89 - 90 def update(self, value: float) -> float: - 91 """Aggregate a new value into the moving average""" - 92 if self.length < self.period: - 93 self.length += 1 - 94 else: - 95 self.accum -= self.prevs[self.pos] - 96 self.prevs[self.pos] = value - 97 self.accum += value - 98 self.pos = (self.pos + 1) % self.period - 99 if self.length < self.period: -100 self.push(np.nan) -101 else: -102 self.push(self.accum / self.period) -103 return self[0] -104 -105 -106class EMA(Indicator): -107 """Exponential Moving Average""" -108 -109 period: float -110 alpha: float -111 smooth_factor: float -112 length: int = 0 -113 prev: float = 0.0 -114 -115 def __init__(self, period: int, *args, **kwargs): -116 super().__init__(*args, **kwargs) -117 alpha = 2.0 if "alpha" not in kwargs else kwargs["alpha"] -118 self.period = period -119 self.alpha = alpha -120 self.smooth_factor = alpha / (1.0 + period) -121 -122 def update(self, value: float) -> float: -123 """Aggregate a new value into the moving average""" -124 self.length += 1 -125 if self.length < self.period: -126 self.prev += value -127 elif self.length == self.period: -128 self.prev += value -129 self.prev /= self.period -130 else: -131 self.prev = (value * self.smooth_factor) + self.prev * ( -132 1.0 - self.smooth_factor -133 ) -134 if self.length < self.period: -135 self.push(np.nan) -136 else: -137 self.push(self.prev) -138 return self[0] -139 -140 -141class ROI(Indicator): -142 """ -143 Return of investment for streaming data. -144 """ -145 -146 prev: float -147 -148 def __init__(self, *args, **kwargs): -149 super().__init__(*args, **kwargs) -150 self.prev = np.nan -151 -152 def update(self, value: float) -> float: -153 """ -154 Updates the indicator. If at least -155 a previous value has been updated before, -156 it starts reporting the return of the -157 investment. -158 """ -159 curr = roi(self.prev, value) -160 self.push(curr) -161 self.prev = value -162 return self[0] -163 -164 -165class RSI(Indicator): -166 gains: MA -167 losses: MA -168 -169 def __init__(self, period: int = 14, **kwargs): -170 args = [] -171 super().__init__(*args, **kwargs) -172 self.gains = MA(period=period) -173 self.losses = MA(period=period) -174 -175 def update(self, open_price: float, close_price: float) -> float: -176 diff = close_price - open_price -177 self.gains.update(diff if diff > 0.0 else 0.0) -178 self.losses.update(-diff if diff < 0.0 else 0.0) -179 if np.isnan(self.losses[0]): -180 self.push(np.nan) -181 else: -182 self.push(100.0 - 100.0 / (1 + self.gains[0] / self.losses[0])) -183 return self[0] -184 -185 -186@define -187class MACDResult: -188 macd: float -189 signal: float -190 hist: float -191 -192 -193class MACD(Indicator): -194 -195 short: EMA -196 long: EMA -197 diff: EMA -198 start: int -199 counter = 0 -200 -201 def __init__( -202 self, -203 short_period: float, -204 long_period: float, -205 diff_period: float, -206 *args, -207 **kwargs -208 ): -209 args = [] -210 super().__init__(*args, **kwargs) -211 self.short = EMA(period=short_period, *args, **kwargs) -212 self.long = EMA(period=long_period, *args, **kwargs) -213 self.diff = EMA(period=diff_period, *args, **kwargs) -214 self.start = long_period if long_period > short_period else short_period -215 -216 def update(self, value: float) -> float: -217 self.counter += 1 -218 self.short.update(value) -219 self.long.update(value) -220 if self.counter >= self.start: -221 diff = self.short[0] - self.long[0] -222 self.diff.update(diff) -223 hist = diff - self.diff[0] -224 self.push(MACDResult(macd=diff, signal=self.diff[0], hist=hist)) -225 else: -226 self.push(MACDResult(macd=np.nan, signal=np.nan, hist=np.nan)) -227 return self[0] -228 -229 -230def roi(initial_value, final_value): -231 """Return on investment""" -232 if initial_value == 0 or np.isnan(initial_value): -233 return np.nan -234 return final_value / initial_value - 1.0 -
12class Indicator: -13 """ -14 Base class to build indicators. It provides a buffer to -15 keep the last `mem_size` values and functions to push -16 new values and retrieve using bracket notation or the -17 function get. -18 -19 All derived classes, call the __init__ method of this class -20 and when an update ocurrs, they push the new value -21 into the memory buffer. -22 -23 Because this class is intended to be used for time series, -24 indices go backwards, being `0` the last updated value, -1 -25 the previous one, -2 the previous of the previos one... -26 If a value is requested with a positive index or a negative -27 index which absolute values is greater than `mem_size`, then -28 an NAN value is returned. -29 """ -30 -31 mem_pos: int -32 mem_data: list -33 mem_size: int -34 -35 def __init__(self, mem_size=1): -36 """ -37 @param mem_size: The size of the memory buffer. The -38 default value is 1. -39 """ -40 self.mem_data = [np.nan] * mem_size -41 self.mem_pos = 0 -42 self.mem_size = mem_size -43 -44 def __getitem__(self, key): -45 """ -46 @param key: 0 for the most recent update -47 a negative number for the n-previous updates -48 -49 A negative number less than -mem_size returns NAN. -50 A positive number returns NAN. -51 """ -52 if key > 0 or -key >= self.mem_size: -53 return np.nan -54 real_pos = (self.mem_pos - key) % self.mem_size -55 return self.mem_data[real_pos] -56 -57 def push(self, value): -58 """ -59 Stores in the buffer `value` as the more recent update. -60 In the current implementation, a ring buffer is used -61 to save these values. -62 -63 @param value: The most recent update -64 """ -65 self.mem_pos = (self.mem_pos - 1) % self.mem_size -66 self.mem_data[self.mem_pos] = value -67 -68 def get(self, key=0) -> float: -69 """The same as `__getitem__`""" -70 return self[key] -
Base class to build indicators. It provides a buffer to
-keep the last mem_size values and functions to push
-new values and retrieve using bracket notation or the
-function get.
All derived classes, call the __init__ method of this class -and when an update ocurrs, they push the new value -into the memory buffer.
- -Because this class is intended to be used for time series,
-indices go backwards, being 0 the last updated value, -1
-the previous one, -2 the previous of the previos one...
-If a value is requested with a positive index or a negative
-index which absolute values is greater than mem_size, then
-an NAN value is returned.
35 def __init__(self, mem_size=1): -36 """ -37 @param mem_size: The size of the memory buffer. The -38 default value is 1. -39 """ -40 self.mem_data = [np.nan] * mem_size -41 self.mem_pos = 0 -42 self.mem_size = mem_size -
@param mem_size: The size of the memory buffer. The - default value is 1.
-57 def push(self, value): -58 """ -59 Stores in the buffer `value` as the more recent update. -60 In the current implementation, a ring buffer is used -61 to save these values. -62 -63 @param value: The most recent update -64 """ -65 self.mem_pos = (self.mem_pos - 1) % self.mem_size -66 self.mem_data[self.mem_pos] = value -
Stores in the buffer value as the more recent update.
-In the current implementation, a ring buffer is used
-to save these values.
@param value: The most recent update
-73class MA(Indicator): - 74 """Moving Average""" - 75 - 76 period: int - 77 prevs: np.ndarray - 78 length: int = 0 - 79 pos: int = 0 - 80 accum: float = 0.0 - 81 - 82 def __init__(self, period: int, *args, **kwargs): - 83 """ - 84 The number of period or window size is required to initialize a MA - 85 object. - 86 """ - 87 super().__init__(*args, **kwargs) - 88 self.period = period - 89 self.prevs = np.zeros(period, dtype=float) - 90 - 91 def update(self, value: float) -> float: - 92 """Aggregate a new value into the moving average""" - 93 if self.length < self.period: - 94 self.length += 1 - 95 else: - 96 self.accum -= self.prevs[self.pos] - 97 self.prevs[self.pos] = value - 98 self.accum += value - 99 self.pos = (self.pos + 1) % self.period -100 if self.length < self.period: -101 self.push(np.nan) -102 else: -103 self.push(self.accum / self.period) -104 return self[0] -
Moving Average
-82 def __init__(self, period: int, *args, **kwargs): -83 """ -84 The number of period or window size is required to initialize a MA -85 object. -86 """ -87 super().__init__(*args, **kwargs) -88 self.period = period -89 self.prevs = np.zeros(period, dtype=float) -
The number of period or window size is required to initialize a MA -object.
-91 def update(self, value: float) -> float: - 92 """Aggregate a new value into the moving average""" - 93 if self.length < self.period: - 94 self.length += 1 - 95 else: - 96 self.accum -= self.prevs[self.pos] - 97 self.prevs[self.pos] = value - 98 self.accum += value - 99 self.pos = (self.pos + 1) % self.period -100 if self.length < self.period: -101 self.push(np.nan) -102 else: -103 self.push(self.accum / self.period) -104 return self[0] -
Aggregate a new value into the moving average
-107class EMA(Indicator): -108 """Exponential Moving Average""" -109 -110 period: float -111 alpha: float -112 smooth_factor: float -113 length: int = 0 -114 prev: float = 0.0 -115 -116 def __init__(self, period: int, *args, **kwargs): -117 super().__init__(*args, **kwargs) -118 alpha = 2.0 if "alpha" not in kwargs else kwargs["alpha"] -119 self.period = period -120 self.alpha = alpha -121 self.smooth_factor = alpha / (1.0 + period) -122 -123 def update(self, value: float) -> float: -124 """Aggregate a new value into the moving average""" -125 self.length += 1 -126 if self.length < self.period: -127 self.prev += value -128 elif self.length == self.period: -129 self.prev += value -130 self.prev /= self.period -131 else: -132 self.prev = (value * self.smooth_factor) + self.prev * ( -133 1.0 - self.smooth_factor -134 ) -135 if self.length < self.period: -136 self.push(np.nan) -137 else: -138 self.push(self.prev) -139 return self[0] -
Exponential Moving Average
-116 def __init__(self, period: int, *args, **kwargs): -117 super().__init__(*args, **kwargs) -118 alpha = 2.0 if "alpha" not in kwargs else kwargs["alpha"] -119 self.period = period -120 self.alpha = alpha -121 self.smooth_factor = alpha / (1.0 + period) -
@param mem_size: The size of the memory buffer. The - default value is 1.
-123 def update(self, value: float) -> float: -124 """Aggregate a new value into the moving average""" -125 self.length += 1 -126 if self.length < self.period: -127 self.prev += value -128 elif self.length == self.period: -129 self.prev += value -130 self.prev /= self.period -131 else: -132 self.prev = (value * self.smooth_factor) + self.prev * ( -133 1.0 - self.smooth_factor -134 ) -135 if self.length < self.period: -136 self.push(np.nan) -137 else: -138 self.push(self.prev) -139 return self[0] -
Aggregate a new value into the moving average
-142class ROI(Indicator): -143 """ -144 Return of investment for streaming data. -145 """ -146 -147 prev: float -148 -149 def __init__(self, *args, **kwargs): -150 super().__init__(*args, **kwargs) -151 self.prev = np.nan -152 -153 def update(self, value: float) -> float: -154 """ -155 Updates the indicator. If at least -156 a previous value has been updated before, -157 it starts reporting the return of the -158 investment. -159 """ -160 curr = roi(self.prev, value) -161 self.push(curr) -162 self.prev = value -163 return self[0] -
Return of investment for streaming data.
-149 def __init__(self, *args, **kwargs): -150 super().__init__(*args, **kwargs) -151 self.prev = np.nan -
@param mem_size: The size of the memory buffer. The - default value is 1.
-153 def update(self, value: float) -> float: -154 """ -155 Updates the indicator. If at least -156 a previous value has been updated before, -157 it starts reporting the return of the -158 investment. -159 """ -160 curr = roi(self.prev, value) -161 self.push(curr) -162 self.prev = value -163 return self[0] -
Updates the indicator. If at least -a previous value has been updated before, -it starts reporting the return of the -investment.
-166class RSI(Indicator): -167 gains: MA -168 losses: MA -169 -170 def __init__(self, period: int = 14, **kwargs): -171 args = [] -172 super().__init__(*args, **kwargs) -173 self.gains = MA(period=period) -174 self.losses = MA(period=period) -175 -176 def update(self, open_price: float, close_price: float) -> float: -177 diff = close_price - open_price -178 self.gains.update(diff if diff > 0.0 else 0.0) -179 self.losses.update(-diff if diff < 0.0 else 0.0) -180 if np.isnan(self.losses[0]): -181 self.push(np.nan) -182 else: -183 self.push(100.0 - 100.0 / (1 + self.gains[0] / self.losses[0])) -184 return self[0] -
Base class to build indicators. It provides a buffer to
-keep the last mem_size values and functions to push
-new values and retrieve using bracket notation or the
-function get.
All derived classes, call the __init__ method of this class -and when an update ocurrs, they push the new value -into the memory buffer.
- -Because this class is intended to be used for time series,
-indices go backwards, being 0 the last updated value, -1
-the previous one, -2 the previous of the previos one...
-If a value is requested with a positive index or a negative
-index which absolute values is greater than mem_size, then
-an NAN value is returned.
170 def __init__(self, period: int = 14, **kwargs): -171 args = [] -172 super().__init__(*args, **kwargs) -173 self.gains = MA(period=period) -174 self.losses = MA(period=period) -
@param mem_size: The size of the memory buffer. The - default value is 1.
-176 def update(self, open_price: float, close_price: float) -> float: -177 diff = close_price - open_price -178 self.gains.update(diff if diff > 0.0 else 0.0) -179 self.losses.update(-diff if diff < 0.0 else 0.0) -180 if np.isnan(self.losses[0]): -181 self.push(np.nan) -182 else: -183 self.push(100.0 - 100.0 / (1 + self.gains[0] / self.losses[0])) -184 return self[0] -
194class MACD(Indicator): -195 -196 short: EMA -197 long: EMA -198 diff: EMA -199 start: int -200 counter = 0 -201 -202 def __init__( -203 self, -204 short_period: float, -205 long_period: float, -206 diff_period: float, -207 *args, -208 **kwargs -209 ): -210 args = [] -211 super().__init__(*args, **kwargs) -212 self.short = EMA(period=short_period, *args, **kwargs) -213 self.long = EMA(period=long_period, *args, **kwargs) -214 self.diff = EMA(period=diff_period, *args, **kwargs) -215 self.start = long_period if long_period > short_period else short_period -216 -217 def update(self, value: float) -> float: -218 self.counter += 1 -219 self.short.update(value) -220 self.long.update(value) -221 if self.counter >= self.start: -222 diff = self.short[0] - self.long[0] -223 self.diff.update(diff) -224 hist = diff - self.diff[0] -225 self.push(MACDResult(macd=diff, signal=self.diff[0], hist=hist)) -226 else: -227 self.push(MACDResult(macd=np.nan, signal=np.nan, hist=np.nan)) -228 return self[0] -
Base class to build indicators. It provides a buffer to
-keep the last mem_size values and functions to push
-new values and retrieve using bracket notation or the
-function get.
All derived classes, call the __init__ method of this class -and when an update ocurrs, they push the new value -into the memory buffer.
- -Because this class is intended to be used for time series,
-indices go backwards, being 0 the last updated value, -1
-the previous one, -2 the previous of the previos one...
-If a value is requested with a positive index or a negative
-index which absolute values is greater than mem_size, then
-an NAN value is returned.
202 def __init__( -203 self, -204 short_period: float, -205 long_period: float, -206 diff_period: float, -207 *args, -208 **kwargs -209 ): -210 args = [] -211 super().__init__(*args, **kwargs) -212 self.short = EMA(period=short_period, *args, **kwargs) -213 self.long = EMA(period=long_period, *args, **kwargs) -214 self.diff = EMA(period=diff_period, *args, **kwargs) -215 self.start = long_period if long_period > short_period else short_period -
@param mem_size: The size of the memory buffer. The - default value is 1.
-217 def update(self, value: float) -> float: -218 self.counter += 1 -219 self.short.update(value) -220 self.long.update(value) -221 if self.counter >= self.start: -222 diff = self.short[0] - self.long[0] -223 self.diff.update(diff) -224 hist = diff - self.diff[0] -225 self.push(MACDResult(macd=diff, signal=self.diff[0], hist=hist)) -226 else: -227 self.push(MACDResult(macd=np.nan, signal=np.nan, hist=np.nan)) -228 return self[0] -
231def roi(initial_value, final_value): -232 """Return on investment""" -233 if initial_value == 0 or np.isnan(initial_value): -234 return np.nan -235 return final_value / initial_value - 1.0 -
Return on investment
-TRADING PORTFOLIO MODELS
- -This module has beed adapted from: -https://github.com/jailop/trading/tree/main/indicators-c%2B%2B
-1""" - 2TRADING PORTFOLIO MODELS - 3 - 4This module has beed adapted from: - 5https://github.com/jailop/trading/tree/main/indicators-c%2B%2B - 6""" - 7 - 8from datetime import datetime - 9from .strategies import Position -10from .indicators import roi -11 -12 -13class Portfolio: -14 """Base Portfolio Class""" -15 -16 initial_cash: float -17 last_position: Position -18 last_exchange: str -19 last_price: float -20 last_ticker: str -21 -22 def __init__(self, cash: float = 1000.0): -23 """Init method""" -24 self.initial_cash = cash -25 self.last_position = Position.STAY -26 self.last_price = 0.0 -27 self.last_ticker = "" -28 self.last_exchange = "" -29 -30 def process( -31 self, -32 time: datetime = datetime.now(), -33 exchange: str = "", -34 ticker: str = "", -35 position: Position = Position.STAY, -36 price: float = 0.0, -37 ): -38 """Process signal""" -39 self.last_ticker = ticker -40 self.last_price = price -41 self.last_position = position -42 self.last_exchange = exchange -43 -44 def valuation(self) -> float: -45 """Default valuation method""" -46 return self.initial_cash -47 -48 def accumulated_return(self) -> float: -49 """Accumulated ROI""" -50 return roi(self.initial_cash, self.valuation()) -51 -52 -53class DummyPortfolio(Portfolio): -54 """ -55 Dummy portfolio is the most basic portfolio model. -56 It works with only one asset. When it receives the buy signal, -57 it uses all the available cash to buy the asset. When it receives -58 the sell signal, it sells all the shares of the asset. -59 """ -60 -61 cash: float -62 share_units: float -63 share_price: float -64 -65 def __init__(self, cash: float = 1000.0): -66 super().__init__(cash) -67 self.cash = cash -68 self.share_units = 0.0 -69 self.share_price = 0.0 -70 -71 def process( -72 self, -73 time: datetime = datetime.now(), -74 exchange: str = "", -75 ticker: str = "", -76 position: Position = Position.STAY, -77 price: float = 0.0, -78 ): -79 super().process(ticker=ticker, position=position, price=price) -80 if position == Position.BUY: -81 if self.cash == 0.0: -82 return -83 self.share_units = self.cash / price -84 self.share_price = price -85 self.cash = 0.0 -86 elif position == Position.SELL: -87 if self.share_units == 0.0: -88 return -89 self.cash = self.share_units * price -90 self.share_price = price -91 self.share_units = 0.0 -92 -93 def valuation(self) -> float: -94 return self.cash if self.cash > 0.0 else (self.share_price * self.share_units) -
14class Portfolio: -15 """Base Portfolio Class""" -16 -17 initial_cash: float -18 last_position: Position -19 last_exchange: str -20 last_price: float -21 last_ticker: str -22 -23 def __init__(self, cash: float = 1000.0): -24 """Init method""" -25 self.initial_cash = cash -26 self.last_position = Position.STAY -27 self.last_price = 0.0 -28 self.last_ticker = "" -29 self.last_exchange = "" -30 -31 def process( -32 self, -33 time: datetime = datetime.now(), -34 exchange: str = "", -35 ticker: str = "", -36 position: Position = Position.STAY, -37 price: float = 0.0, -38 ): -39 """Process signal""" -40 self.last_ticker = ticker -41 self.last_price = price -42 self.last_position = position -43 self.last_exchange = exchange -44 -45 def valuation(self) -> float: -46 """Default valuation method""" -47 return self.initial_cash -48 -49 def accumulated_return(self) -> float: -50 """Accumulated ROI""" -51 return roi(self.initial_cash, self.valuation()) -
Base Portfolio Class
-23 def __init__(self, cash: float = 1000.0): -24 """Init method""" -25 self.initial_cash = cash -26 self.last_position = Position.STAY -27 self.last_price = 0.0 -28 self.last_ticker = "" -29 self.last_exchange = "" -
Init method
-31 def process( -32 self, -33 time: datetime = datetime.now(), -34 exchange: str = "", -35 ticker: str = "", -36 position: Position = Position.STAY, -37 price: float = 0.0, -38 ): -39 """Process signal""" -40 self.last_ticker = ticker -41 self.last_price = price -42 self.last_position = position -43 self.last_exchange = exchange -
Process signal
-54class DummyPortfolio(Portfolio): -55 """ -56 Dummy portfolio is the most basic portfolio model. -57 It works with only one asset. When it receives the buy signal, -58 it uses all the available cash to buy the asset. When it receives -59 the sell signal, it sells all the shares of the asset. -60 """ -61 -62 cash: float -63 share_units: float -64 share_price: float -65 -66 def __init__(self, cash: float = 1000.0): -67 super().__init__(cash) -68 self.cash = cash -69 self.share_units = 0.0 -70 self.share_price = 0.0 -71 -72 def process( -73 self, -74 time: datetime = datetime.now(), -75 exchange: str = "", -76 ticker: str = "", -77 position: Position = Position.STAY, -78 price: float = 0.0, -79 ): -80 super().process(ticker=ticker, position=position, price=price) -81 if position == Position.BUY: -82 if self.cash == 0.0: -83 return -84 self.share_units = self.cash / price -85 self.share_price = price -86 self.cash = 0.0 -87 elif position == Position.SELL: -88 if self.share_units == 0.0: -89 return -90 self.cash = self.share_units * price -91 self.share_price = price -92 self.share_units = 0.0 -93 -94 def valuation(self) -> float: -95 return self.cash if self.cash > 0.0 else (self.share_price * self.share_units) -
Dummy portfolio is the most basic portfolio model. -It works with only one asset. When it receives the buy signal, -it uses all the available cash to buy the asset. When it receives -the sell signal, it sells all the shares of the asset.
-66 def __init__(self, cash: float = 1000.0): -67 super().__init__(cash) -68 self.cash = cash -69 self.share_units = 0.0 -70 self.share_price = 0.0 -
Init method
-72 def process( -73 self, -74 time: datetime = datetime.now(), -75 exchange: str = "", -76 ticker: str = "", -77 position: Position = Position.STAY, -78 price: float = 0.0, -79 ): -80 super().process(ticker=ticker, position=position, price=price) -81 if position == Position.BUY: -82 if self.cash == 0.0: -83 return -84 self.share_units = self.cash / price -85 self.share_price = price -86 self.cash = 0.0 -87 elif position == Position.SELL: -88 if self.share_units == 0.0: -89 return -90 self.cash = self.share_units * price -91 self.share_price = price -92 self.share_units = 0.0 -
Process signal
-94 def valuation(self) -> float: -95 return self.cash if self.cash > 0.0 else (self.share_price * self.share_units) -
Default valuation method
-Strategies
-1"""Strategies""" - 2 - 3from enum import Enum - 4from datetime import datetime - 5from attrs import define - 6 - 7 - 8class Position(Enum): - 9 """Trading Positions""" -10 -11 STAY = 1 -12 BUY = 2 -13 SELL = 3 -14 -15 -16@define -17class StrategySignal: -18 """To report computations of an strategy""" -19 -20 time: datetime = datetime.now() -21 position: Position = Position.STAY -22 price: float = 0.0 -23 ticker: str = "" -24 exchange: str = "" -25 -26 -27class Strategy: -28 """Base class for strategies""" -29 -30 def __init__(self, *args, **kwargs): -31 """ -32 Init Method. Included for future support. -33 """ -34 -35 def evaluate(self, *args, **kwargs) -> StrategySignal: -36 """ -37 Evaluate method. Include for future support -38 """ -39 # The default position is STAY -40 return StrategySignal() -
Trading Positions
-17@define -18class StrategySignal: -19 """To report computations of an strategy""" -20 -21 time: datetime = datetime.now() -22 position: Position = Position.STAY -23 price: float = 0.0 -24 ticker: str = "" -25 exchange: str = "" -
To report computations of an strategy
-2def __init__(self, time=attr_dict['time'].default, position=attr_dict['position'].default, price=attr_dict['price'].default, ticker=attr_dict['ticker'].default, exchange=attr_dict['exchange'].default): -3 self.time = time -4 self.position = position -5 self.price = price -6 self.ticker = ticker -7 self.exchange = exchange -
Method generated by attrs for class StrategySignal.
-28class Strategy: -29 """Base class for strategies""" -30 -31 def __init__(self, *args, **kwargs): -32 """ -33 Init Method. Included for future support. -34 """ -35 -36 def evaluate(self, *args, **kwargs) -> StrategySignal: -37 """ -38 Evaluate method. Include for future support -39 """ -40 # The default position is STAY -41 return StrategySignal() -
Base class for strategies
-31 def __init__(self, *args, **kwargs): -32 """ -33 Init Method. Included for future support. -34 """ -
Init Method. Included for future support.
-A collection of bottraders
-1"""A collection of bottraders""" - 2 - 3from typing import Union - 4from datetime import datetime - 5from attrs import define, asdict - 6from .datastreamers import DataStreamer - 7from .portfolios import Portfolio - 8from .strategies import Strategy, Position, StrategySignal - 9from .indicators import roi -10 -11 -12@define -13class TradingIteration: -14 """Used to report results from a trading iteration""" -15 -16 signal: StrategySignal -17 data: dict -18 roi: Union[float, None] -19 portfolio_value: float -20 accumulated_roi: Union[float, None] -21 -22 -23class Trader: -24 """Base class""" -25 -26 portfolio: Portfolio -27 datastream: DataStreamer -28 strategy: Strategy -29 last_result: Union[TradingIteration, None] = None -30 last_valuation: float = 0.0 -31 -32 def __init__( -33 self, -34 strategy: Strategy, -35 portfolio: Portfolio, -36 datastream: DataStreamer, -37 ): -38 """Init method""" -39 self.datastream = datastream -40 self.portfolio = portfolio -41 self.strategy = strategy -42 -43 def next(self) -> bool: -44 """Perfoms a trading iteration""" -45 obs = self.datastream.next() -46 if obs is None: -47 return False -48 signal = self.strategy.evaluate(**obs) -49 self.portfolio.process(**asdict(signal)) -50 self.last_result = TradingIteration( -51 signal=signal, -52 data=obs, -53 roi=roi(self.last_valuation, self.portfolio.valuation()), -54 portfolio_value=self.portfolio.valuation(), -55 accumulated_roi=self.portfolio.accumulated_return(), -56 ) -57 self.last_valuation = self.portfolio.valuation() -58 return True -59 -60 def status(self) -> TradingIteration: -61 """Trader last result""" -62 return self.last_result -63 -64 def run(self): -65 """A default runner""" -66 # A nice header -67 print( -68 "{:26} {:4} {:>10} {:>10} {:>10} {:>10}".format( # pylint: disable=consider-using-f-string -69 "Time", "Pos.", "Price", "ROI", "Valuation", "Accum.ROI" -70 ) -71 ) -72 # Run the back-testing -73 while self.next(): -74 status = self.status() -75 if status.signal.position != Position.STAY: -76 # A nice output -77 print( -78 f"{status.signal.time} {status.signal.position.name:4} {status.data['close']:10.2f} " -79 + f"{status.roi * 100.0:10.2f}% {status.portfolio_value:10.2f} " -80 + f"{status.accumulated_roi * 100.0:10.2f}%" -81 ) -
13@define -14class TradingIteration: -15 """Used to report results from a trading iteration""" -16 -17 signal: StrategySignal -18 data: dict -19 roi: Union[float, None] -20 portfolio_value: float -21 accumulated_roi: Union[float, None] -
Used to report results from a trading iteration
-2def __init__(self, signal, data, roi, portfolio_value, accumulated_roi): -3 self.signal = signal -4 self.data = data -5 self.roi = roi -6 self.portfolio_value = portfolio_value -7 self.accumulated_roi = accumulated_roi -
Method generated by attrs for class TradingIteration.
-24class Trader: -25 """Base class""" -26 -27 portfolio: Portfolio -28 datastream: DataStreamer -29 strategy: Strategy -30 last_result: Union[TradingIteration, None] = None -31 last_valuation: float = 0.0 -32 -33 def __init__( -34 self, -35 strategy: Strategy, -36 portfolio: Portfolio, -37 datastream: DataStreamer, -38 ): -39 """Init method""" -40 self.datastream = datastream -41 self.portfolio = portfolio -42 self.strategy = strategy -43 -44 def next(self) -> bool: -45 """Perfoms a trading iteration""" -46 obs = self.datastream.next() -47 if obs is None: -48 return False -49 signal = self.strategy.evaluate(**obs) -50 self.portfolio.process(**asdict(signal)) -51 self.last_result = TradingIteration( -52 signal=signal, -53 data=obs, -54 roi=roi(self.last_valuation, self.portfolio.valuation()), -55 portfolio_value=self.portfolio.valuation(), -56 accumulated_roi=self.portfolio.accumulated_return(), -57 ) -58 self.last_valuation = self.portfolio.valuation() -59 return True -60 -61 def status(self) -> TradingIteration: -62 """Trader last result""" -63 return self.last_result -64 -65 def run(self): -66 """A default runner""" -67 # A nice header -68 print( -69 "{:26} {:4} {:>10} {:>10} {:>10} {:>10}".format( # pylint: disable=consider-using-f-string -70 "Time", "Pos.", "Price", "ROI", "Valuation", "Accum.ROI" -71 ) -72 ) -73 # Run the back-testing -74 while self.next(): -75 status = self.status() -76 if status.signal.position != Position.STAY: -77 # A nice output -78 print( -79 f"{status.signal.time} {status.signal.position.name:4} {status.data['close']:10.2f} " -80 + f"{status.roi * 100.0:10.2f}% {status.portfolio_value:10.2f} " -81 + f"{status.accumulated_roi * 100.0:10.2f}%" -82 ) -
Base class
-33 def __init__( -34 self, -35 strategy: Strategy, -36 portfolio: Portfolio, -37 datastream: DataStreamer, -38 ): -39 """Init method""" -40 self.datastream = datastream -41 self.portfolio = portfolio -42 self.strategy = strategy -
Init method
-44 def next(self) -> bool: -45 """Perfoms a trading iteration""" -46 obs = self.datastream.next() -47 if obs is None: -48 return False -49 signal = self.strategy.evaluate(**obs) -50 self.portfolio.process(**asdict(signal)) -51 self.last_result = TradingIteration( -52 signal=signal, -53 data=obs, -54 roi=roi(self.last_valuation, self.portfolio.valuation()), -55 portfolio_value=self.portfolio.valuation(), -56 accumulated_roi=self.portfolio.accumulated_return(), -57 ) -58 self.last_valuation = self.portfolio.valuation() -59 return True -
Perfoms a trading iteration
-65 def run(self): -66 """A default runner""" -67 # A nice header -68 print( -69 "{:26} {:4} {:>10} {:>10} {:>10} {:>10}".format( # pylint: disable=consider-using-f-string -70 "Time", "Pos.", "Price", "ROI", "Valuation", "Accum.ROI" -71 ) -72 ) -73 # Run the back-testing -74 while self.next(): -75 status = self.status() -76 if status.signal.position != Position.STAY: -77 # A nice output -78 print( -79 f"{status.signal.time} {status.signal.position.name:4} {status.data['close']:10.2f} " -80 + f"{status.roi * 100.0:10.2f}% {status.portfolio_value:10.2f} " -81 + f"{status.accumulated_roi * 100.0:10.2f}%" -82 ) -
A default runner
-To install:
\n\npip install git+https://github.com/jailop/pybottrader.git\nData Streamers
\n"}, "pybottrader.datastreamers.DataStreamer": {"fullname": "pybottrader.datastreamers.DataStreamer", "modulename": "pybottrader.datastreamers", "qualname": "DataStreamer", "kind": "class", "doc": "A data streamer abstract class
\n"}, "pybottrader.datastreamers.DataStreamer.__init__": {"fullname": "pybottrader.datastreamers.DataStreamer.__init__", "modulename": "pybottrader.datastreamers", "qualname": "DataStreamer.__init__", "kind": "function", "doc": "Init method
\n", "signature": "()"}, "pybottrader.datastreamers.DataStreamer.next": {"fullname": "pybottrader.datastreamers.DataStreamer.next", "modulename": "pybottrader.datastreamers", "qualname": "DataStreamer.next", "kind": "function", "doc": "Next method
\n", "signature": "(self) -> Optional[dict]:", "funcdef": "def"}, "pybottrader.datastreamers.YFinanceStreamer": {"fullname": "pybottrader.datastreamers.YFinanceStreamer", "modulename": "pybottrader.datastreamers", "qualname": "YFinanceStreamer", "kind": "class", "doc": "Using Yahoo Finance to retrieve data
\n", "bases": "DataStreamer"}, "pybottrader.datastreamers.YFinanceStreamer.__init__": {"fullname": "pybottrader.datastreamers.YFinanceStreamer.__init__", "modulename": "pybottrader.datastreamers", "qualname": "YFinanceStreamer.__init__", "kind": "function", "doc": "Init method
\n", "signature": "(symbol, *args, **kwargs)"}, "pybottrader.datastreamers.YFinanceStreamer.index": {"fullname": "pybottrader.datastreamers.YFinanceStreamer.index", "modulename": "pybottrader.datastreamers", "qualname": "YFinanceStreamer.index", "kind": "variable", "doc": "\n", "default_value": "0"}, "pybottrader.datastreamers.YFinanceStreamer.data": {"fullname": "pybottrader.datastreamers.YFinanceStreamer.data", "modulename": "pybottrader.datastreamers", "qualname": "YFinanceStreamer.data", "kind": "variable", "doc": "\n", "annotation": ": pandas.core.frame.DataFrame"}, "pybottrader.datastreamers.YFinanceStreamer.next": {"fullname": "pybottrader.datastreamers.YFinanceStreamer.next", "modulename": "pybottrader.datastreamers", "qualname": "YFinanceStreamer.next", "kind": "function", "doc": "Next method
\n", "signature": "(self) -> Optional[dict]:", "funcdef": "def"}, "pybottrader.datastreamers.CSVFileStreamer": {"fullname": "pybottrader.datastreamers.CSVFileStreamer", "modulename": "pybottrader.datastreamers", "qualname": "CSVFileStreamer", "kind": "class", "doc": "An dataframe file streamer
\n", "bases": "DataStreamer"}, "pybottrader.datastreamers.CSVFileStreamer.__init__": {"fullname": "pybottrader.datastreamers.CSVFileStreamer.__init__", "modulename": "pybottrader.datastreamers", "qualname": "CSVFileStreamer.__init__", "kind": "function", "doc": "Init method
\n", "signature": "(filename: str)"}, "pybottrader.datastreamers.CSVFileStreamer.data": {"fullname": "pybottrader.datastreamers.CSVFileStreamer.data", "modulename": "pybottrader.datastreamers", "qualname": "CSVFileStreamer.data", "kind": "variable", "doc": "\n", "annotation": ": pandas.core.frame.DataFrame"}, "pybottrader.datastreamers.CSVFileStreamer.index": {"fullname": "pybottrader.datastreamers.CSVFileStreamer.index", "modulename": "pybottrader.datastreamers", "qualname": "CSVFileStreamer.index", "kind": "variable", "doc": "\n", "annotation": ": int"}, "pybottrader.datastreamers.CSVFileStreamer.next": {"fullname": "pybottrader.datastreamers.CSVFileStreamer.next", "modulename": "pybottrader.datastreamers", "qualname": "CSVFileStreamer.next", "kind": "function", "doc": "Next method
\n", "signature": "(self) -> Optional[dict]:", "funcdef": "def"}, "pybottrader.indicators": {"fullname": "pybottrader.indicators", "modulename": "pybottrader.indicators", "kind": "module", "doc": "Moving Average\nImplementation adopted from:\nhttps://github.com/jailop/trading/tree/main/indicators-c++
\n"}, "pybottrader.indicators.Indicator": {"fullname": "pybottrader.indicators.Indicator", "modulename": "pybottrader.indicators", "qualname": "Indicator", "kind": "class", "doc": "Base class to build indicators. It provides a buffer to\nkeep the last mem_size values and functions to push\nnew values and retrieve using bracket notation or the\nfunction get.
All derived classes, call the __init__ method of this class\nand when an update ocurrs, they push the new value\ninto the memory buffer.
\n\nBecause this class is intended to be used for time series,\nindices go backwards, being 0 the last updated value, -1\nthe previous one, -2 the previous of the previos one...\nIf a value is requested with a positive index or a negative\nindex which absolute values is greater than mem_size, then\nan NAN value is returned.
@param mem_size: The size of the memory buffer. The\n default value is 1.
\n", "signature": "(mem_size=1)"}, "pybottrader.indicators.Indicator.mem_pos": {"fullname": "pybottrader.indicators.Indicator.mem_pos", "modulename": "pybottrader.indicators", "qualname": "Indicator.mem_pos", "kind": "variable", "doc": "\n", "annotation": ": int"}, "pybottrader.indicators.Indicator.mem_data": {"fullname": "pybottrader.indicators.Indicator.mem_data", "modulename": "pybottrader.indicators", "qualname": "Indicator.mem_data", "kind": "variable", "doc": "\n", "annotation": ": list"}, "pybottrader.indicators.Indicator.mem_size": {"fullname": "pybottrader.indicators.Indicator.mem_size", "modulename": "pybottrader.indicators", "qualname": "Indicator.mem_size", "kind": "variable", "doc": "\n", "annotation": ": int"}, "pybottrader.indicators.Indicator.push": {"fullname": "pybottrader.indicators.Indicator.push", "modulename": "pybottrader.indicators", "qualname": "Indicator.push", "kind": "function", "doc": "Stores in the buffer value as the more recent update.\nIn the current implementation, a ring buffer is used\nto save these values.
@param value: The most recent update
\n", "signature": "(self, value):", "funcdef": "def"}, "pybottrader.indicators.Indicator.get": {"fullname": "pybottrader.indicators.Indicator.get", "modulename": "pybottrader.indicators", "qualname": "Indicator.get", "kind": "function", "doc": "The same as __getitem__
Moving Average
\n", "bases": "Indicator"}, "pybottrader.indicators.MA.__init__": {"fullname": "pybottrader.indicators.MA.__init__", "modulename": "pybottrader.indicators", "qualname": "MA.__init__", "kind": "function", "doc": "The number of period or window size is required to initialize a MA\nobject.
\n", "signature": "(period: int, *args, **kwargs)"}, "pybottrader.indicators.MA.period": {"fullname": "pybottrader.indicators.MA.period", "modulename": "pybottrader.indicators", "qualname": "MA.period", "kind": "variable", "doc": "\n", "annotation": ": int"}, "pybottrader.indicators.MA.prevs": {"fullname": "pybottrader.indicators.MA.prevs", "modulename": "pybottrader.indicators", "qualname": "MA.prevs", "kind": "variable", "doc": "\n", "annotation": ": numpy.ndarray"}, "pybottrader.indicators.MA.length": {"fullname": "pybottrader.indicators.MA.length", "modulename": "pybottrader.indicators", "qualname": "MA.length", "kind": "variable", "doc": "\n", "annotation": ": int", "default_value": "0"}, "pybottrader.indicators.MA.pos": {"fullname": "pybottrader.indicators.MA.pos", "modulename": "pybottrader.indicators", "qualname": "MA.pos", "kind": "variable", "doc": "\n", "annotation": ": int", "default_value": "0"}, "pybottrader.indicators.MA.accum": {"fullname": "pybottrader.indicators.MA.accum", "modulename": "pybottrader.indicators", "qualname": "MA.accum", "kind": "variable", "doc": "\n", "annotation": ": float", "default_value": "0.0"}, "pybottrader.indicators.MA.update": {"fullname": "pybottrader.indicators.MA.update", "modulename": "pybottrader.indicators", "qualname": "MA.update", "kind": "function", "doc": "Aggregate a new value into the moving average
\n", "signature": "(self, value: float) -> float:", "funcdef": "def"}, "pybottrader.indicators.EMA": {"fullname": "pybottrader.indicators.EMA", "modulename": "pybottrader.indicators", "qualname": "EMA", "kind": "class", "doc": "Exponential Moving Average
\n", "bases": "Indicator"}, "pybottrader.indicators.EMA.__init__": {"fullname": "pybottrader.indicators.EMA.__init__", "modulename": "pybottrader.indicators", "qualname": "EMA.__init__", "kind": "function", "doc": "@param mem_size: The size of the memory buffer. The\n default value is 1.
\n", "signature": "(period: int, *args, **kwargs)"}, "pybottrader.indicators.EMA.period": {"fullname": "pybottrader.indicators.EMA.period", "modulename": "pybottrader.indicators", "qualname": "EMA.period", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.indicators.EMA.alpha": {"fullname": "pybottrader.indicators.EMA.alpha", "modulename": "pybottrader.indicators", "qualname": "EMA.alpha", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.indicators.EMA.smooth_factor": {"fullname": "pybottrader.indicators.EMA.smooth_factor", "modulename": "pybottrader.indicators", "qualname": "EMA.smooth_factor", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.indicators.EMA.length": {"fullname": "pybottrader.indicators.EMA.length", "modulename": "pybottrader.indicators", "qualname": "EMA.length", "kind": "variable", "doc": "\n", "annotation": ": int", "default_value": "0"}, "pybottrader.indicators.EMA.prev": {"fullname": "pybottrader.indicators.EMA.prev", "modulename": "pybottrader.indicators", "qualname": "EMA.prev", "kind": "variable", "doc": "\n", "annotation": ": float", "default_value": "0.0"}, "pybottrader.indicators.EMA.update": {"fullname": "pybottrader.indicators.EMA.update", "modulename": "pybottrader.indicators", "qualname": "EMA.update", "kind": "function", "doc": "Aggregate a new value into the moving average
\n", "signature": "(self, value: float) -> float:", "funcdef": "def"}, "pybottrader.indicators.ROI": {"fullname": "pybottrader.indicators.ROI", "modulename": "pybottrader.indicators", "qualname": "ROI", "kind": "class", "doc": "Return of investment for streaming data.
\n", "bases": "Indicator"}, "pybottrader.indicators.ROI.__init__": {"fullname": "pybottrader.indicators.ROI.__init__", "modulename": "pybottrader.indicators", "qualname": "ROI.__init__", "kind": "function", "doc": "@param mem_size: The size of the memory buffer. The\n default value is 1.
\n", "signature": "(*args, **kwargs)"}, "pybottrader.indicators.ROI.prev": {"fullname": "pybottrader.indicators.ROI.prev", "modulename": "pybottrader.indicators", "qualname": "ROI.prev", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.indicators.ROI.update": {"fullname": "pybottrader.indicators.ROI.update", "modulename": "pybottrader.indicators", "qualname": "ROI.update", "kind": "function", "doc": "Updates the indicator. If at least\na previous value has been updated before,\nit starts reporting the return of the\ninvestment.
\n", "signature": "(self, value: float) -> float:", "funcdef": "def"}, "pybottrader.indicators.RSI": {"fullname": "pybottrader.indicators.RSI", "modulename": "pybottrader.indicators", "qualname": "RSI", "kind": "class", "doc": "Base class to build indicators. It provides a buffer to\nkeep the last mem_size values and functions to push\nnew values and retrieve using bracket notation or the\nfunction get.
All derived classes, call the __init__ method of this class\nand when an update ocurrs, they push the new value\ninto the memory buffer.
\n\nBecause this class is intended to be used for time series,\nindices go backwards, being 0 the last updated value, -1\nthe previous one, -2 the previous of the previos one...\nIf a value is requested with a positive index or a negative\nindex which absolute values is greater than mem_size, then\nan NAN value is returned.
@param mem_size: The size of the memory buffer. The\n default value is 1.
\n", "signature": "(period: int = 14, **kwargs)"}, "pybottrader.indicators.RSI.gains": {"fullname": "pybottrader.indicators.RSI.gains", "modulename": "pybottrader.indicators", "qualname": "RSI.gains", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.indicators.MA"}, "pybottrader.indicators.RSI.losses": {"fullname": "pybottrader.indicators.RSI.losses", "modulename": "pybottrader.indicators", "qualname": "RSI.losses", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.indicators.MA"}, "pybottrader.indicators.RSI.update": {"fullname": "pybottrader.indicators.RSI.update", "modulename": "pybottrader.indicators", "qualname": "RSI.update", "kind": "function", "doc": "\n", "signature": "(self, open_price: float, close_price: float) -> float:", "funcdef": "def"}, "pybottrader.indicators.MACDResult": {"fullname": "pybottrader.indicators.MACDResult", "modulename": "pybottrader.indicators", "qualname": "MACDResult", "kind": "class", "doc": "\n"}, "pybottrader.indicators.MACDResult.__init__": {"fullname": "pybottrader.indicators.MACDResult.__init__", "modulename": "pybottrader.indicators", "qualname": "MACDResult.__init__", "kind": "function", "doc": "Method generated by attrs for class MACDResult.
\n", "signature": "(macd: float, signal: float, hist: float)"}, "pybottrader.indicators.MACDResult.macd": {"fullname": "pybottrader.indicators.MACDResult.macd", "modulename": "pybottrader.indicators", "qualname": "MACDResult.macd", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.indicators.MACDResult.signal": {"fullname": "pybottrader.indicators.MACDResult.signal", "modulename": "pybottrader.indicators", "qualname": "MACDResult.signal", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.indicators.MACDResult.hist": {"fullname": "pybottrader.indicators.MACDResult.hist", "modulename": "pybottrader.indicators", "qualname": "MACDResult.hist", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.indicators.MACD": {"fullname": "pybottrader.indicators.MACD", "modulename": "pybottrader.indicators", "qualname": "MACD", "kind": "class", "doc": "Base class to build indicators. It provides a buffer to\nkeep the last mem_size values and functions to push\nnew values and retrieve using bracket notation or the\nfunction get.
All derived classes, call the __init__ method of this class\nand when an update ocurrs, they push the new value\ninto the memory buffer.
\n\nBecause this class is intended to be used for time series,\nindices go backwards, being 0 the last updated value, -1\nthe previous one, -2 the previous of the previos one...\nIf a value is requested with a positive index or a negative\nindex which absolute values is greater than mem_size, then\nan NAN value is returned.
@param mem_size: The size of the memory buffer. The\n default value is 1.
\n", "signature": "(\tshort_period: float,\tlong_period: float,\tdiff_period: float,\t*args,\t**kwargs)"}, "pybottrader.indicators.MACD.short": {"fullname": "pybottrader.indicators.MACD.short", "modulename": "pybottrader.indicators", "qualname": "MACD.short", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.indicators.EMA"}, "pybottrader.indicators.MACD.long": {"fullname": "pybottrader.indicators.MACD.long", "modulename": "pybottrader.indicators", "qualname": "MACD.long", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.indicators.EMA"}, "pybottrader.indicators.MACD.diff": {"fullname": "pybottrader.indicators.MACD.diff", "modulename": "pybottrader.indicators", "qualname": "MACD.diff", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.indicators.EMA"}, "pybottrader.indicators.MACD.start": {"fullname": "pybottrader.indicators.MACD.start", "modulename": "pybottrader.indicators", "qualname": "MACD.start", "kind": "variable", "doc": "\n", "annotation": ": int"}, "pybottrader.indicators.MACD.counter": {"fullname": "pybottrader.indicators.MACD.counter", "modulename": "pybottrader.indicators", "qualname": "MACD.counter", "kind": "variable", "doc": "\n", "default_value": "0"}, "pybottrader.indicators.MACD.update": {"fullname": "pybottrader.indicators.MACD.update", "modulename": "pybottrader.indicators", "qualname": "MACD.update", "kind": "function", "doc": "\n", "signature": "(self, value: float) -> float:", "funcdef": "def"}, "pybottrader.indicators.roi": {"fullname": "pybottrader.indicators.roi", "modulename": "pybottrader.indicators", "qualname": "roi", "kind": "function", "doc": "Return on investment
\n", "signature": "(initial_value, final_value):", "funcdef": "def"}, "pybottrader.portfolios": {"fullname": "pybottrader.portfolios", "modulename": "pybottrader.portfolios", "kind": "module", "doc": "TRADING PORTFOLIO MODELS
\n\nThis module has beed adapted from:\nhttps://github.com/jailop/trading/tree/main/indicators-c%2B%2B
\n"}, "pybottrader.portfolios.Portfolio": {"fullname": "pybottrader.portfolios.Portfolio", "modulename": "pybottrader.portfolios", "qualname": "Portfolio", "kind": "class", "doc": "Base Portfolio Class
\n"}, "pybottrader.portfolios.Portfolio.__init__": {"fullname": "pybottrader.portfolios.Portfolio.__init__", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.__init__", "kind": "function", "doc": "Init method
\n", "signature": "(cash: float = 1000.0)"}, "pybottrader.portfolios.Portfolio.initial_cash": {"fullname": "pybottrader.portfolios.Portfolio.initial_cash", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.initial_cash", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.portfolios.Portfolio.last_position": {"fullname": "pybottrader.portfolios.Portfolio.last_position", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.last_position", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.strategies.Position"}, "pybottrader.portfolios.Portfolio.last_exchange": {"fullname": "pybottrader.portfolios.Portfolio.last_exchange", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.last_exchange", "kind": "variable", "doc": "\n", "annotation": ": str"}, "pybottrader.portfolios.Portfolio.last_price": {"fullname": "pybottrader.portfolios.Portfolio.last_price", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.last_price", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.portfolios.Portfolio.last_ticker": {"fullname": "pybottrader.portfolios.Portfolio.last_ticker", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.last_ticker", "kind": "variable", "doc": "\n", "annotation": ": str"}, "pybottrader.portfolios.Portfolio.process": {"fullname": "pybottrader.portfolios.Portfolio.process", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.process", "kind": "function", "doc": "Process signal
\n", "signature": "(\tself,\ttime: datetime.datetime = datetime.datetime(2024, 12, 8, 13, 50, 29, 829525),\texchange: str = '',\tticker: str = '',\tposition: pybottrader.strategies.Position = <Position.STAY: 1>,\tprice: float = 0.0):", "funcdef": "def"}, "pybottrader.portfolios.Portfolio.valuation": {"fullname": "pybottrader.portfolios.Portfolio.valuation", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.valuation", "kind": "function", "doc": "Default valuation method
\n", "signature": "(self) -> float:", "funcdef": "def"}, "pybottrader.portfolios.Portfolio.accumulated_return": {"fullname": "pybottrader.portfolios.Portfolio.accumulated_return", "modulename": "pybottrader.portfolios", "qualname": "Portfolio.accumulated_return", "kind": "function", "doc": "Accumulated ROI
\n", "signature": "(self) -> float:", "funcdef": "def"}, "pybottrader.portfolios.DummyPortfolio": {"fullname": "pybottrader.portfolios.DummyPortfolio", "modulename": "pybottrader.portfolios", "qualname": "DummyPortfolio", "kind": "class", "doc": "Dummy portfolio is the most basic portfolio model.\nIt works with only one asset. When it receives the buy signal,\nit uses all the available cash to buy the asset. When it receives\nthe sell signal, it sells all the shares of the asset.
\n", "bases": "Portfolio"}, "pybottrader.portfolios.DummyPortfolio.__init__": {"fullname": "pybottrader.portfolios.DummyPortfolio.__init__", "modulename": "pybottrader.portfolios", "qualname": "DummyPortfolio.__init__", "kind": "function", "doc": "Init method
\n", "signature": "(cash: float = 1000.0)"}, "pybottrader.portfolios.DummyPortfolio.cash": {"fullname": "pybottrader.portfolios.DummyPortfolio.cash", "modulename": "pybottrader.portfolios", "qualname": "DummyPortfolio.cash", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.portfolios.DummyPortfolio.share_units": {"fullname": "pybottrader.portfolios.DummyPortfolio.share_units", "modulename": "pybottrader.portfolios", "qualname": "DummyPortfolio.share_units", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.portfolios.DummyPortfolio.share_price": {"fullname": "pybottrader.portfolios.DummyPortfolio.share_price", "modulename": "pybottrader.portfolios", "qualname": "DummyPortfolio.share_price", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.portfolios.DummyPortfolio.process": {"fullname": "pybottrader.portfolios.DummyPortfolio.process", "modulename": "pybottrader.portfolios", "qualname": "DummyPortfolio.process", "kind": "function", "doc": "Process signal
\n", "signature": "(\tself,\ttime: datetime.datetime = datetime.datetime(2024, 12, 8, 13, 50, 29, 829535),\texchange: str = '',\tticker: str = '',\tposition: pybottrader.strategies.Position = <Position.STAY: 1>,\tprice: float = 0.0):", "funcdef": "def"}, "pybottrader.portfolios.DummyPortfolio.valuation": {"fullname": "pybottrader.portfolios.DummyPortfolio.valuation", "modulename": "pybottrader.portfolios", "qualname": "DummyPortfolio.valuation", "kind": "function", "doc": "Default valuation method
\n", "signature": "(self) -> float:", "funcdef": "def"}, "pybottrader.strategies": {"fullname": "pybottrader.strategies", "modulename": "pybottrader.strategies", "kind": "module", "doc": "Strategies
\n"}, "pybottrader.strategies.Position": {"fullname": "pybottrader.strategies.Position", "modulename": "pybottrader.strategies", "qualname": "Position", "kind": "class", "doc": "Trading Positions
\n", "bases": "enum.Enum"}, "pybottrader.strategies.Position.STAY": {"fullname": "pybottrader.strategies.Position.STAY", "modulename": "pybottrader.strategies", "qualname": "Position.STAY", "kind": "variable", "doc": "\n", "default_value": "<Position.STAY: 1>"}, "pybottrader.strategies.Position.BUY": {"fullname": "pybottrader.strategies.Position.BUY", "modulename": "pybottrader.strategies", "qualname": "Position.BUY", "kind": "variable", "doc": "\n", "default_value": "<Position.BUY: 2>"}, "pybottrader.strategies.Position.SELL": {"fullname": "pybottrader.strategies.Position.SELL", "modulename": "pybottrader.strategies", "qualname": "Position.SELL", "kind": "variable", "doc": "\n", "default_value": "<Position.SELL: 3>"}, "pybottrader.strategies.StrategySignal": {"fullname": "pybottrader.strategies.StrategySignal", "modulename": "pybottrader.strategies", "qualname": "StrategySignal", "kind": "class", "doc": "To report computations of an strategy
\n"}, "pybottrader.strategies.StrategySignal.__init__": {"fullname": "pybottrader.strategies.StrategySignal.__init__", "modulename": "pybottrader.strategies", "qualname": "StrategySignal.__init__", "kind": "function", "doc": "Method generated by attrs for class StrategySignal.
\n", "signature": "(\ttime: datetime.datetime = datetime.datetime(2024, 12, 8, 13, 50, 29, 829062),\tposition: pybottrader.strategies.Position = <Position.STAY: 1>,\tprice: float = 0.0,\tticker: str = '',\texchange: str = '')"}, "pybottrader.strategies.StrategySignal.time": {"fullname": "pybottrader.strategies.StrategySignal.time", "modulename": "pybottrader.strategies", "qualname": "StrategySignal.time", "kind": "variable", "doc": "\n", "annotation": ": datetime.datetime"}, "pybottrader.strategies.StrategySignal.position": {"fullname": "pybottrader.strategies.StrategySignal.position", "modulename": "pybottrader.strategies", "qualname": "StrategySignal.position", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.strategies.Position"}, "pybottrader.strategies.StrategySignal.price": {"fullname": "pybottrader.strategies.StrategySignal.price", "modulename": "pybottrader.strategies", "qualname": "StrategySignal.price", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.strategies.StrategySignal.ticker": {"fullname": "pybottrader.strategies.StrategySignal.ticker", "modulename": "pybottrader.strategies", "qualname": "StrategySignal.ticker", "kind": "variable", "doc": "\n", "annotation": ": str"}, "pybottrader.strategies.StrategySignal.exchange": {"fullname": "pybottrader.strategies.StrategySignal.exchange", "modulename": "pybottrader.strategies", "qualname": "StrategySignal.exchange", "kind": "variable", "doc": "\n", "annotation": ": str"}, "pybottrader.strategies.Strategy": {"fullname": "pybottrader.strategies.Strategy", "modulename": "pybottrader.strategies", "qualname": "Strategy", "kind": "class", "doc": "Base class for strategies
\n"}, "pybottrader.strategies.Strategy.__init__": {"fullname": "pybottrader.strategies.Strategy.__init__", "modulename": "pybottrader.strategies", "qualname": "Strategy.__init__", "kind": "function", "doc": "Init Method. Included for future support.
\n", "signature": "(*args, **kwargs)"}, "pybottrader.strategies.Strategy.evaluate": {"fullname": "pybottrader.strategies.Strategy.evaluate", "modulename": "pybottrader.strategies", "qualname": "Strategy.evaluate", "kind": "function", "doc": "Evaluate method. Include for future support
\n", "signature": "(self, *args, **kwargs) -> pybottrader.strategies.StrategySignal:", "funcdef": "def"}, "pybottrader.traders": {"fullname": "pybottrader.traders", "modulename": "pybottrader.traders", "kind": "module", "doc": "A collection of bottraders
\n"}, "pybottrader.traders.TradingIteration": {"fullname": "pybottrader.traders.TradingIteration", "modulename": "pybottrader.traders", "qualname": "TradingIteration", "kind": "class", "doc": "Used to report results from a trading iteration
\n"}, "pybottrader.traders.TradingIteration.__init__": {"fullname": "pybottrader.traders.TradingIteration.__init__", "modulename": "pybottrader.traders", "qualname": "TradingIteration.__init__", "kind": "function", "doc": "Method generated by attrs for class TradingIteration.
\n", "signature": "(\tsignal: pybottrader.strategies.StrategySignal,\tdata: dict,\troi: Optional[float],\tportfolio_value: float,\taccumulated_roi: Optional[float])"}, "pybottrader.traders.TradingIteration.signal": {"fullname": "pybottrader.traders.TradingIteration.signal", "modulename": "pybottrader.traders", "qualname": "TradingIteration.signal", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.strategies.StrategySignal"}, "pybottrader.traders.TradingIteration.data": {"fullname": "pybottrader.traders.TradingIteration.data", "modulename": "pybottrader.traders", "qualname": "TradingIteration.data", "kind": "variable", "doc": "\n", "annotation": ": dict"}, "pybottrader.traders.TradingIteration.roi": {"fullname": "pybottrader.traders.TradingIteration.roi", "modulename": "pybottrader.traders", "qualname": "TradingIteration.roi", "kind": "variable", "doc": "\n", "annotation": ": Optional[float]"}, "pybottrader.traders.TradingIteration.portfolio_value": {"fullname": "pybottrader.traders.TradingIteration.portfolio_value", "modulename": "pybottrader.traders", "qualname": "TradingIteration.portfolio_value", "kind": "variable", "doc": "\n", "annotation": ": float"}, "pybottrader.traders.TradingIteration.accumulated_roi": {"fullname": "pybottrader.traders.TradingIteration.accumulated_roi", "modulename": "pybottrader.traders", "qualname": "TradingIteration.accumulated_roi", "kind": "variable", "doc": "\n", "annotation": ": Optional[float]"}, "pybottrader.traders.Trader": {"fullname": "pybottrader.traders.Trader", "modulename": "pybottrader.traders", "qualname": "Trader", "kind": "class", "doc": "Base class
\n"}, "pybottrader.traders.Trader.__init__": {"fullname": "pybottrader.traders.Trader.__init__", "modulename": "pybottrader.traders", "qualname": "Trader.__init__", "kind": "function", "doc": "Init method
\n", "signature": "(\tstrategy: pybottrader.strategies.Strategy,\tportfolio: pybottrader.portfolios.Portfolio,\tdatastream: pybottrader.datastreamers.DataStreamer)"}, "pybottrader.traders.Trader.portfolio": {"fullname": "pybottrader.traders.Trader.portfolio", "modulename": "pybottrader.traders", "qualname": "Trader.portfolio", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.portfolios.Portfolio"}, "pybottrader.traders.Trader.datastream": {"fullname": "pybottrader.traders.Trader.datastream", "modulename": "pybottrader.traders", "qualname": "Trader.datastream", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.datastreamers.DataStreamer"}, "pybottrader.traders.Trader.strategy": {"fullname": "pybottrader.traders.Trader.strategy", "modulename": "pybottrader.traders", "qualname": "Trader.strategy", "kind": "variable", "doc": "\n", "annotation": ": pybottrader.strategies.Strategy"}, "pybottrader.traders.Trader.last_result": {"fullname": "pybottrader.traders.Trader.last_result", "modulename": "pybottrader.traders", "qualname": "Trader.last_result", "kind": "variable", "doc": "\n", "annotation": ": Optional[pybottrader.traders.TradingIteration]", "default_value": "None"}, "pybottrader.traders.Trader.last_valuation": {"fullname": "pybottrader.traders.Trader.last_valuation", "modulename": "pybottrader.traders", "qualname": "Trader.last_valuation", "kind": "variable", "doc": "\n", "annotation": ": float", "default_value": "0.0"}, "pybottrader.traders.Trader.next": {"fullname": "pybottrader.traders.Trader.next", "modulename": "pybottrader.traders", "qualname": "Trader.next", "kind": "function", "doc": "Perfoms a trading iteration
\n", "signature": "(self) -> bool:", "funcdef": "def"}, "pybottrader.traders.Trader.status": {"fullname": "pybottrader.traders.Trader.status", "modulename": "pybottrader.traders", "qualname": "Trader.status", "kind": "function", "doc": "Trader last result
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{"pybottrader.indicators.MA.__init__": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"pybottrader.portfolios.DummyPortfolio": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pybottrader.indicators.EMA": {"tf": 1}}, "df": 1}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pybottrader.strategies.Strategy.evaluate": {"tf": 1}}, "df": 1}}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; - - // mirrored in build-search-index.js (part 1) - // Also split on html tags. this is a cheap heuristic, but good enough. - elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/); - - let searchIndex; - if (docs._isPrebuiltIndex) { - console.info("using precompiled search index"); - searchIndex = elasticlunr.Index.load(docs); - } else { - console.time("building search index"); - // mirrored in build-search-index.js (part 2) - searchIndex = elasticlunr(function () { - this.pipeline.remove(elasticlunr.stemmer); - this.pipeline.remove(elasticlunr.stopWordFilter); - this.addField("qualname"); - this.addField("fullname"); - this.addField("annotation"); - this.addField("default_value"); - this.addField("signature"); - this.addField("bases"); - this.addField("doc"); - this.setRef("fullname"); - }); - for (let doc of docs) { - searchIndex.addDoc(doc); - } - console.timeEnd("building search index"); - } - - return (term) => searchIndex.search(term, { - fields: { - qualname: {boost: 4}, - fullname: {boost: 2}, - annotation: {boost: 2}, - default_value: {boost: 2}, - signature: {boost: 2}, - bases: {boost: 2}, - doc: {boost: 1}, - }, - expand: true - }); -})(); \ No newline at end of file diff --git a/src/pybottrader/__init__.py b/pybottrader/__init__.py similarity index 93% rename from src/pybottrader/__init__.py rename to pybottrader/__init__.py index 6ac4efd..4e9db6c 100644 --- a/src/pybottrader/__init__.py +++ b/pybottrader/__init__.py @@ -18,3 +18,5 @@ pip install pybottrader ``` """ + +from .indicators import * # Import the C++ module directly diff --git a/src/pybottrader/datastreamers/__init__.py b/pybottrader/datastreamers/__init__.py similarity index 100% rename from src/pybottrader/datastreamers/__init__.py rename to pybottrader/datastreamers/__init__.py diff --git a/src/pybottrader/datastreamers/yfinance.py b/pybottrader/datastreamers/yfinance.py similarity index 100% rename from src/pybottrader/datastreamers/yfinance.py rename to pybottrader/datastreamers/yfinance.py diff --git a/pybottrader/indicators/CMakeLists.txt b/pybottrader/indicators/CMakeLists.txt new file mode 100644 index 0000000..f12fdf6 --- /dev/null +++ b/pybottrader/indicators/CMakeLists.txt @@ -0,0 +1,35 @@ +cmake_minimum_required(VERSION 3.15) +project(indicators) + +if(NOT TARGET pybind11::module) + find_package(pybind11 REQUIRED) +endif() + +# Platform specific flags +if(MSVC) + # Windows-specific flags + add_compile_options(/W4 /EHsc) +else() + # Linux/macOS flags + add_compile_options(-Wall -Wextra) +endif() + +# Create module +pybind11_add_module(_indicators + src/bindings.cpp +) + +# Include directories +target_include_directories(_indicators + PRIVATE + ${CMAKE_CURRENT_SOURCE_DIR}/include +) + +# Platform-specific link options +if(APPLE) + target_link_options(_indicators PRIVATE -undefined dynamic_lookup) +endif() + +install(TARGETS _indicators + LIBRARY DESTINATION pybottrader/indicators +) diff --git a/pybottrader/indicators/__init__.py b/pybottrader/indicators/__init__.py new file mode 100644 index 0000000..ac2106a --- /dev/null +++ b/pybottrader/indicators/__init__.py @@ -0,0 +1 @@ +from ._indicators import * # This will import ATR and other symbols directly into indicators namespace diff --git a/pybottrader/indicators/include/indicators.hpp b/pybottrader/indicators/include/indicators.hpp new file mode 100644 index 0000000..84c0ad9 --- /dev/null +++ b/pybottrader/indicators/include/indicators.hpp @@ -0,0 +1,208 @@ +/** + * Library of indicators + * + */ + +#pragma once +#include