gbencke / pyastrotrader Goto Github PK

Hi..
I am getting error in Predict.price.change.ipynb
at following line #9 as below:

StockPrices[column_name] = StockPricesDask.map_partitions(lambda df : df.apply(lambda x : int(get_degree_for_planet(x, current_planet) / 3), axis =1), meta='int').compute(scheduler='processes')

Error:

KeyError Traceback (most recent call last)
in
5 column_name="ASTRO_{}_POSITION".format(PLANETS[current_planet]).upper()
6 StockPricesDask = dd.from_pandas(StockPrices, npartitions=NPARTITIONS)
----> 7 StockPrices[column_name] = StockPricesDask.map_partitions(lambda df : df.apply(lambda x : int(get_degree_for_planet(x, current_planet) / 3), axis =1), meta='int').compute(scheduler='processes')
8 StockPrices[column_name] = pd.to_numeric(StockPrices[column_name], downcast='float', errors='coerce')
9 astro_columns.append(column_name)

~/anaconda3/lib/python3.7/site-packages/dask/base.py in compute(self, **kwargs)
164 dask.base.compute
165 """
--> 166 (result,) = compute(self, traverse=False, **kwargs)
167 return result
168

~/anaconda3/lib/python3.7/site-packages/dask/base.py in compute(*args, **kwargs)
442 postcomputes.append(x.dask_postcompute())
443
--> 444 results = schedule(dsk, keys, **kwargs)
445 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
446

~/anaconda3/lib/python3.7/site-packages/dask/multiprocessing.py in get(dsk, keys, num_workers, func_loads, func_dumps, optimize_graph, pool, **kwargs)
216 pack_exception=pack_exception,
217 raise_exception=reraise,
--> 218 **kwargs
219 )
220 finally:

~/anaconda3/lib/python3.7/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
484 _execute_task(task, data) # Re-execute locally
485 else:
--> 486 raise_exception(exc, tb)
487 res, worker_id = loads(res_info)
488 state["cache"][key] = res

~/anaconda3/lib/python3.7/site-packages/dask/local.py in reraise(exc, tb)
314 if exc.traceback is not tb:
315 raise exc.with_traceback(tb)
--> 316 raise exc
317
318

~/anaconda3/lib/python3.7/site-packages/dask/local.py in execute_task()
220 try:
221 task, data = loads(task_info)
--> 222 result = _execute_task(task, data)
223 id = get_id()
224 result = dumps((result, id))

~/anaconda3/lib/python3.7/site-packages/dask/core.py in _execute_task()
119 # temporaries by their reference count and can execute certain
120 # operations in-place.
--> 121 return func(*(_execute_task(a, cache) for a in args))
122 elif not ishashable(arg):
123 return arg

~/anaconda3/lib/python3.7/site-packages/dask/optimization.py in call()
986 if not len(args) == len(self.inkeys):
987 raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args)))
--> 988 return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
989
990 def reduce(self):

~/anaconda3/lib/python3.7/site-packages/dask/core.py in get()
149 for key in toposort(dsk):
150 task = dsk[key]
--> 151 result = _execute_task(task, cache)
152 cache[key] = result
153 result = _execute_task(out, cache)

~/anaconda3/lib/python3.7/site-packages/dask/core.py in _execute_task()
119 # temporaries by their reference count and can execute certain
120 # operations in-place.
--> 121 return func(*(_execute_task(a, cache) for a in args))
122 elif not ishashable(arg):
123 return arg

~/anaconda3/lib/python3.7/site-packages/dask/utils.py in apply()
28 def apply(func, args, kwargs=None):
29 if kwargs:
---> 30 return func(*args, **kwargs)
31 else:
32 return func(*args)

~/anaconda3/lib/python3.7/site-packages/dask/dataframe/core.py in apply_and_enforce()
5129 func = kwargs.pop("_func")
5130 meta = kwargs.pop("_meta")
-> 5131 df = func(*args, **kwargs)
5132 if is_dataframe_like(df) or is_series_like(df) or is_index_like(df):
5133 if not len(df):

in ()
5 column_name="ASTRO_{}_POSITION".format(PLANETS[current_planet]).upper()
6 StockPricesDask = dd.from_pandas(StockPrices, npartitions=NPARTITIONS)
----> 7 StockPrices[column_name] = StockPricesDask.map_partitions(lambda df : df.apply(lambda x : int(get_degree_for_planet(x, current_planet) / 3), axis =1), meta='int').compute(scheduler='processes')
8 StockPrices[column_name] = pd.to_numeric(StockPrices[column_name], downcast='float', errors='coerce')
9 astro_columns.append(column_name)

~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in apply()
6876 kwds=kwds,
6877 )
-> 6878 return op.get_result()
6879
6880 def applymap(self, func) -> "DataFrame":

~/anaconda3/lib/python3.7/site-packages/pandas/core/apply.py in get_result()
184 return self.apply_raw()
185
--> 186 return self.apply_standard()
187
188 def apply_empty_result(self):

~/anaconda3/lib/python3.7/site-packages/pandas/core/apply.py in apply_standard()
294 try:
295 result = libreduction.compute_reduction(
--> 296 values, self.f, axis=self.axis, dummy=dummy, labels=labels
297 )
298 except ValueError as err:

pandas/_libs/reduction.pyx in pandas._libs.reduction.compute_reduction()

pandas/_libs/reduction.pyx in pandas._libs.reduction.Reducer.get_result()

in ()
5 column_name="ASTRO_{}_POSITION".format(PLANETS[current_planet]).upper()
6 StockPricesDask = dd.from_pandas(StockPrices, npartitions=NPARTITIONS)
----> 7 StockPrices[column_name] = StockPricesDask.map_partitions(lambda df : df.apply(lambda x : int(get_degree_for_planet(x, current_planet) / 3), axis =1), meta='int').compute(scheduler='processes')
8 StockPrices[column_name] = pd.to_numeric(StockPrices[column_name], downcast='float', errors='coerce')
9 astro_columns.append(column_name)

~/Downloads/pyAstroTrader/notebooks/helpers.py in get_degree_for_planet()
195
196 def get_degree_for_planet(row, planet):
--> 197 c_chart = charts[row['CorrectedDate']]
198 return get_degree(c_chart, planet)
199

KeyError: '2020-05-19'

What is the accuracy achieved with the current model? It would be very interesting to delve deep and motivating factor if you have found some correlation with astrological data.

This link is incorrect:

It's supposed to be https://www.amazon.com/Bull-Bear-Planets-Financial-Astrology/dp/1475980027

Hi,

I am quite interested in this project. When can I see the charts?

Regards

Trying to get this succesfully deployed and running into issues with calculate.py where ret_flag is being used as a comparison operator but is a tuple, and not sure which of the 5 values is the relevant one

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-9-f6ffd9385682> in <module>
      4                                             DEFAULT_CONFIG)
      5 
----> 6     asset_natal_chart = calculate_chart(asset_natal_chart_input)
      7     dates_to_generate = list(StockPrices['CorrectedDate'])

/home/daniel/pyAstroTrader/pyastrotrader/__init__.py in calculate_chart(input_json)
     14     input_data = check_input(input_json)
     15     config = load_config(input_data)
---> 16     output = generate_chart(config, input_data)
     17     return output
     18 

/home/daniel/pyAstroTrader/pyastrotrader/calculate/calculate.py in generate_chart(config, input_data)
    137     output['input'] = input_data
    138     output['intermediate'] = intermediate
--> 139     output = calculate_planets(input_data, intermediate, output, config)
    140     output = calculate_main_chart(input_data, intermediate, output)
    141     output = calculate_planets_in_houses(output)

/home/daniel/pyAstroTrader/pyastrotrader/calculate/calculate.py in calculate_planets(input_data, intermediate, output, config)
     85                             'zodiac'][x] + ":" + str(x)
     86                     output['planets']['planets_degree'][
---> 87                         i] = ret_flag[0] - deg_low
     88                     output['planets']['planets_degree_ut'][i] = ret_flag[0]
     89                     if ret_flag[3] < 0:

TypeError: unsupported operand type(s) for -: 'tuple' and 'float'

Great project would love to take a deeper look. wrote in c++ perl and php for 25 years. do you happen to have quick install cheat sheet? (never used notebooks.) sorry if that sounded lame. thanks Jim Brown