def generate_minute_test_data(first_day,
last_day,
starting_open,
starting_volume,
multipliers_list,
path):
"""
Utility method to generate fake minute-level CSV data.
:param first_day: first trading day
:param last_day: last trading day
:param starting_open: first open value, raw value.
:param starting_volume: first volume value, raw value.
:param multipliers_list: ordered list of pd.Timestamp -> float, one per day
in the range
:param path: path to save the CSV
:return: None
"""
full_minutes = BcolzMinuteBarWriter.full_minutes_for_days(
first_day, last_day)
minutes_count = len(full_minutes)
minutes = TradingEnvironment.instance().minutes_for_days_in_range(
first_day, last_day)
o = np.zeros(minutes_count, dtype=np.uint32)
h = np.zeros(minutes_count, dtype=np.uint32)
l = np.zeros(minutes_count, dtype=np.uint32)
c = np.zeros(minutes_count, dtype=np.uint32)
v = np.zeros(minutes_count, dtype=np.uint32)
last_open = starting_open * 1000
last_volume = starting_volume
for minute in minutes:
# ugly, but works
idx = full_minutes.searchsorted(minute)
new_open = last_open + round((random.random() * 5), 2)
o[idx] = new_open
h[idx] = new_open + round((random.random() * 10000), 2)
l[idx] = new_open - round((random.random() * 10000), 2)
c[idx] = (h[idx] + l[idx]) / 2
v[idx] = int(last_volume + (random.randrange(-10, 10) * 1e4))
last_open = o[idx]
last_volume = v[idx]
# now deal with multipliers
if len(multipliers_list) > 0:
for idx, multiplier_info in enumerate(multipliers_list):
start_idx = idx * 390
end_idx = start_idx + 390
# dividing by the multipler because we're going backwards
# and generating the original data that will then be adjusted.
o[start_idx:end_idx] /= multiplier_info[1]
h[start_idx:end_idx] /= multiplier_info[1]
l[start_idx:end_idx] /= multiplier_info[1]
c[start_idx:end_idx] /= multiplier_info[1]
v[start_idx:end_idx] *= multiplier_info[1]
df = pd.DataFrame({
"open": o,
"high": h,
"low": l,
"close": c,
"volume": v
}, columns=[
"open",
"high",
"low",
"close",
"volume"
], index=minutes)
df.to_csv(path, index_label="minute")
def generate_daily_test_data(first_day,
last_day,
starting_open,
starting_volume,
multipliers_list,
path):
days = TradingEnvironment.instance().days_in_range(first_day, last_day)
days_count = len(days)
o = np.zeros(days_count, dtype=np.uint32)
h = np.zeros(days_count, dtype=np.uint32)
l = np.zeros(days_count, dtype=np.uint32)
c = np.zeros(days_count, dtype=np.uint32)
v = np.zeros(days_count, dtype=np.uint32)
last_open = starting_open * 1000
last_volume = starting_volume
for idx in range(days_count):
new_open = last_open + round((random.random() * 5), 2)
o[idx] = new_open
h[idx] = new_open + round((random.random() * 10000), 2)
l[idx] = new_open - round((random.random() * 10000), 2)
c[idx] = (h[idx] + l[idx]) / 2
v[idx] = int(last_volume + (random.randrange(-10, 10) * 1e4))
last_open = o[idx]
last_volume = v[idx]
# now deal with multipliers
if len(multipliers_list) > 0:
range_start = 0
for multiplier_info in multipliers_list:
range_end = days.searchsorted(multiplier_info[0])
# dividing by the multiplier because we're going backwards
# and generating the original data that will then be adjusted.
o[range_start:range_end] /= multiplier_info[1]
h[range_start:range_end] /= multiplier_info[1]
l[range_start:range_end] /= multiplier_info[1]
c[range_start:range_end] /= multiplier_info[1]
v[range_start:range_end] *= multiplier_info[1]
range_start = range_end
df = pd.DataFrame({
"open": o,
"high": h,
"low": l,
"close": c,
"volume": v
}, columns=[
"open",
"high",
"low",
"close",
"volume"
], index=days)
df.to_csv(path, index_label="day")