def start_new_initial_eval(self, wi: int, hm_key: str):
self.iter_counter += 1 # up iter counter on each new config started
config = {
**{
"long": deepcopy(self.hm[hm_key]["long"]["config"]),
"short": deepcopy(self.hm[hm_key]["short"]["config"]),
},
**{
k: self.config[k]
for k in [
"starting_balance", "latency_simulation_ms", "market_type"
]
},
**{
"symbol": self.symbols[0],
"initial_eval_key": hm_key,
"config_no": self.iter_counter
},
}
line = f"starting new initial eval {config['config_no']} of {self.n_harmonies} "
if self.do_long:
line += " - long: " + " ".join([
f"{e[0][:2]}{e[0][-2:]}" + str(round_dynamic(e[1], 3))
for e in sorted(self.hm[hm_key]["long"]["config"].items())
])
if self.do_short:
line += " - short: " + " ".join([
f"{e[0][:2]}{e[0][-2:]}" + str(round_dynamic(e[1], 3))
for e in sorted(self.hm[hm_key]["short"]["config"].items())
])
logging.info(line)
config["market_specific_settings"] = self.market_specific_settings[
config["symbol"]]
config[
"ticks_cache_fname"] = f"{self.bt_dir}/{config['symbol']}/{self.ticks_cache_fname}"
config["passivbot_mode"] = self.config["passivbot_mode"]
self.workers[wi] = {
"config":
deepcopy(config),
"task":
self.pool.apply_async(backtest_wrap,
args=(deepcopy(config), self.ticks_caches)),
"id_key":
config["config_no"],
}
self.unfinished_evals[config["config_no"]] = {
"config": deepcopy(config),
"single_results": {},
"in_progress": set([self.symbols[0]]),
}
self.hm[hm_key]["long"]["score"] = "in_progress"
self.hm[hm_key]["short"]["score"] = "in_progress"
def compress_float(n: float, d: int) -> str:
if n / 10**d >= 1:
n = round(n)
else:
n = round_dynamic(n, d)
nstr = format_float(n)
if nstr.startswith("0."):
nstr = nstr[1:]
elif nstr.startswith("-0."):
nstr = "-" + nstr[2:]
elif nstr.endswith(".0"):
nstr = nstr[:-2]
return nstr
def round_values(xs, n: int):
if type(xs) in [float, np.float64]:
return round_dynamic(xs, n)
if type(xs) == dict:
return {k: round_values(xs[k], n) for k in xs}
if type(xs) == list:
return [round_values(x, n) for x in xs]
if type(xs) == np.ndarray:
return numpyize([round_values(x, n) for x in xs])
if type(xs) == tuple:
return tuple([round_values(x, n) for x in xs])
if type(xs) == OrderedDict:
return OrderedDict([(k, round_values(xs[k], n)) for k in xs])
return xs
async def main():
logging.basicConfig(
format="%(asctime)s %(levelname)-8s %(message)s",
level=logging.INFO,
datefmt="%Y-%m-%dT%H:%M:%S",
)
parser = argparse.ArgumentParser(
prog="auto profit transfer",
description=
"automatically transfer percentage of profits from futures wallet to spot wallet",
)
parser.add_argument("user",
type=str,
help="user/account_name defined in api-keys.json")
parser.add_argument(
"-p",
"--percentage",
type=float,
required=False,
default=0.5,
dest="percentage",
help="per uno, i.e. 0.02==2%. default=0.5",
)
args = parser.parse_args()
config = get_template_live_config()
config["user"] = args.user
config["symbol"] = "BTCUSDT" # dummy symbol
config["market_type"] = "futures"
bot = await create_binance_bot(config)
transfer_log_fpath = make_get_filepath(
os.path.join("logs",
f"automatic_profit_transfer_log_{config['user']}.json"))
try:
already_transferred_ids = set(json.load(open(transfer_log_fpath)))
logging.info(f"loaded already transferred IDs: {transfer_log_fpath}")
except:
already_transferred_ids = set()
logging.info(f"no previous transfers to load")
while True:
now = (await bot.public_get(bot.endpoints["time"]))["serverTime"]
try:
income = await bot.get_all_income(start_time=now -
1000 * 60 * 60 * 24)
except Exception as e:
logging.error(f"failed fetching income {e}")
traceback.print_exc()
income = []
income = [
e for e in income
if e["transaction_id"] not in already_transferred_ids
]
profit = sum([e["income"] for e in income])
to_transfer = round_dynamic(profit * args.percentage, 4)
if to_transfer > 0:
try:
transferred = await bot.private_post(
bot.endpoints["futures_transfer"],
{
"asset": "USDT",
"amount": to_transfer,
"type": 2
},
base_endpoint=bot.spot_base_endpoint,
)
logging.info(
f"income: {profit} transferred {to_transfer} USDT")
already_transferred_ids.update(
[e["transaction_id"] for e in income])
json.dump(list(already_transferred_ids),
open(transfer_log_fpath, "w"))
except Exception as e:
logging.error(f"failed transferring {e}")
traceback.print_exc()
else:
logging.info("nothing to transfer")
sleep(60 * 60)
def start_new_harmony(self, wi: int):
self.iter_counter += 1 # up iter counter on each new config started
template = get_template_live_config(self.config["passivbot_mode"])
new_harmony = {
**{
"long": deepcopy(template["long"]),
"short": deepcopy(template["short"]),
},
**{
k: self.config[k]
for k in [
"starting_balance", "latency_simulation_ms", "market_type"
]
},
**{
"symbol": self.symbols[0],
"config_no": self.iter_counter
},
}
new_harmony["long"]["enabled"] = self.do_long
new_harmony["short"]["enabled"] = self.do_short
for key in self.long_bounds:
if np.random.random() < self.hm_considering_rate:
# take note randomly from harmony memory
new_note_long = self.hm[np.random.choice(list(
self.hm))]["long"]["config"][key]
new_note_short = self.hm[np.random.choice(list(
self.hm))]["short"]["config"][key]
if np.random.random() < self.pitch_adjusting_rate:
# tweak note
new_note_long = new_note_long + self.bandwidth * (
np.random.random() -
0.5) * abs(self.long_bounds[key][0] -
self.long_bounds[key][1])
new_note_short = new_note_short + self.bandwidth * (
np.random.random() -
0.5) * abs(self.short_bounds[key][0] -
self.short_bounds[key][1])
# ensure note is within bounds
new_note_long = max(
self.long_bounds[key][0],
min(self.long_bounds[key][1], new_note_long))
new_note_short = max(
self.short_bounds[key][0],
min(self.short_bounds[key][1], new_note_short))
else:
# new random note
new_note_long = np.random.uniform(self.long_bounds[key][0],
self.long_bounds[key][1])
new_note_short = np.random.uniform(self.short_bounds[key][0],
self.short_bounds[key][1])
new_harmony["long"][key] = new_note_long
new_harmony["short"][key] = new_note_short
logging.debug(
f"starting new harmony {new_harmony['config_no']} - long " +
" ".join([
str(round_dynamic(e[1], 3))
for e in sorted(new_harmony["long"].items())
]) + " - short: " + " ".join([
str(round_dynamic(e[1], 3))
for e in sorted(new_harmony["short"].items())
]))
new_harmony[
"market_specific_settings"] = self.market_specific_settings[
new_harmony["symbol"]]
new_harmony[
"ticks_cache_fname"] = f"{self.bt_dir}/{new_harmony['symbol']}/{self.ticks_cache_fname}"
new_harmony["passivbot_mode"] = self.config["passivbot_mode"]
self.workers[wi] = {
"config":
deepcopy(new_harmony),
"task":
self.pool.apply_async(backtest_wrap,
args=(deepcopy(new_harmony),
self.ticks_caches)),
"id_key":
new_harmony["config_no"],
}
self.unfinished_evals[new_harmony["config_no"]] = {
"config": deepcopy(new_harmony),
"single_results": {},
"in_progress": set([self.symbols[0]]),
}
def post_process(self, wi: int):
# a worker has finished a job; process it
cfg = deepcopy(self.workers[wi]["config"])
id_key = self.workers[wi]["id_key"]
symbol = cfg["symbol"]
self.unfinished_evals[id_key]["single_results"][symbol] = self.workers[
wi]["task"].get()
self.unfinished_evals[id_key]["in_progress"].remove(symbol)
results = deepcopy(self.unfinished_evals[id_key]["single_results"])
if set(results) == set(self.symbols):
# completed multisymbol iter
adgs_long = [v["adg_long"] for v in results.values()]
adg_mean_long = np.mean(adgs_long)
PAD_std_long_raw = np.mean(
[v["pa_distance_std_long"] for v in results.values()])
PAD_std_long = np.mean([
max(self.config["maximum_pa_distance_std_long"],
v["pa_distance_std_long"]) for v in results.values()
])
PAD_mean_long_raw = np.mean(
[v["pa_distance_mean_long"] for v in results.values()])
PAD_mean_long = np.mean([
max(self.config["maximum_pa_distance_mean_long"],
v["pa_distance_mean_long"]) for v in results.values()
])
adg_DGstd_ratios_long = [
v["adg_DGstd_ratio_long"] for v in results.values()
]
adg_DGstd_ratios_long_mean = np.mean(adg_DGstd_ratios_long)
adgs_short = [v["adg_short"] for v in results.values()]
adg_mean_short = np.mean(adgs_short)
PAD_std_short_raw = np.mean(
[v["pa_distance_std_short"] for v in results.values()])
PAD_std_short = np.mean([
max(self.config["maximum_pa_distance_std_short"],
v["pa_distance_std_short"]) for v in results.values()
])
PAD_mean_short_raw = np.mean(
[v["pa_distance_mean_short"] for v in results.values()])
PAD_mean_short = np.mean([
max(self.config["maximum_pa_distance_mean_short"],
v["pa_distance_mean_short"]) for v in results.values()
])
adg_DGstd_ratios_short = [
v["adg_DGstd_ratio_short"] for v in results.values()
]
adg_DGstd_ratios_short_mean = np.mean(adg_DGstd_ratios_short)
if self.config["score_formula"] == "adg_PAD_mean":
score_long = -adg_mean_long * min(
1.0, self.config["maximum_pa_distance_mean_long"] /
PAD_mean_long)
score_short = -adg_mean_short * min(
1.0, self.config["maximum_pa_distance_mean_short"] /
PAD_mean_short)
elif self.config["score_formula"] == "adg_PAD_std":
score_long = -adg_mean_long / max(
self.config["maximum_pa_distance_std_long"], PAD_std_long)
score_short = -adg_mean_short / max(
self.config["maximum_pa_distance_std_short"],
PAD_std_short)
elif self.config["score_formula"] == "adg_DGstd_ratio":
score_long = -adg_DGstd_ratios_long_mean
score_short = -adg_DGstd_ratios_short_mean
elif self.config["score_formula"] == "adg_mean":
score_long = -adg_mean_long
score_short = -adg_mean_short
elif self.config["score_formula"] == "adg_min":
score_long = -min(adgs_long)
score_short = -min(adgs_short)
elif self.config["score_formula"] == "adg_PAD_std_min":
# best worst score
scores_long = [
v["adg_long"] /
max(v["pa_distance_std_long"],
self.config["maximum_pa_distance_std_long"])
for v in results.values()
]
score_long = -min(scores_long)
scores_short = [
v["adg_short"] /
max(v["pa_distance_std_short"],
self.config["maximum_pa_distance_std_short"])
for v in results.values()
]
score_short = -min(scores_short)
else:
raise Exception(
f"unknown score formula {self.config['score_formula']}")
line = f"completed multisymbol iter {cfg['config_no']} "
if self.do_long:
line += f"- adg long {adg_mean_long:.6f} PAD long {PAD_mean_long:.6f} std long "
line += f"{PAD_std_long:.5f} score long {score_long:.7f} "
if self.do_short:
line += f"- adg short {adg_mean_short:.6f} PAD short {PAD_mean_short:.6f} std short "
line += f"{PAD_std_short:.5f} score short {score_short:.7f}"
logging.debug(line)
# check whether initial eval or new harmony
if "initial_eval_key" in cfg:
self.hm[cfg["initial_eval_key"]]["long"]["score"] = score_long
self.hm[
cfg["initial_eval_key"]]["short"]["score"] = score_short
else:
# check if better than worst in harmony memory
worst_key_long = sorted(
self.hm,
key=lambda x: self.hm[x]["long"]["score"]
if type(self.hm[x]["long"]["score"]) != str else -np.inf,
)[-1]
if self.do_long and score_long < self.hm[worst_key_long][
"long"]["score"]:
logging.debug(
f"improved long harmony, prev score " +
f"{self.hm[worst_key_long]['long']['score']:.7f} new score {score_long:.7f} - "
+ " ".join([
str(round_dynamic(e[1], 3))
for e in sorted(cfg["long"].items())
]))
self.hm[worst_key_long]["long"] = {
"config": deepcopy(cfg["long"]),
"score": score_long,
}
json.dump(
self.hm,
open(
f"{self.results_fpath}hm_{cfg['config_no']:06}.json",
"w"),
indent=4,
sort_keys=True,
)
worst_key_short = sorted(
self.hm,
key=lambda x: self.hm[x]["short"]["score"]
if type(self.hm[x]["short"]["score"]) != str else -np.inf,
)[-1]
if self.do_short and score_short < self.hm[worst_key_short][
"short"]["score"]:
logging.debug(
f"improved short harmony, prev score " +
f"{self.hm[worst_key_short]['short']['score']:.7f} new score {score_short:.7f} - "
+ " ".join([
str(round_dynamic(e[1], 3))
for e in sorted(cfg["short"].items())
]), )
self.hm[worst_key_short]["short"] = {
"config": deepcopy(cfg["short"]),
"score": score_short,
}
json.dump(
self.hm,
open(
f"{self.results_fpath}hm_{cfg['config_no']:06}.json",
"w"),
indent=4,
sort_keys=True,
)
best_key_long = sorted(
self.hm,
key=lambda x: self.hm[x]["long"]["score"]
if type(self.hm[x]["long"]["score"]) != str else np.inf,
)[0]
best_key_short = sorted(
self.hm,
key=lambda x: self.hm[x]["short"]["score"]
if type(self.hm[x]["short"]["score"]) != str else np.inf,
)[0]
best_config = {
"long": deepcopy(self.hm[best_key_long]["long"]["config"]),
"short": deepcopy(self.hm[best_key_short]["short"]["config"]),
}
best_config["result"] = {
"symbol": f"{len(self.symbols)}_symbols",
"exchange": self.config["exchange"],
"start_date": self.config["start_date"],
"end_date": self.config["end_date"],
}
tmp_fname = f"{self.results_fpath}{cfg['config_no']:06}_best_config"
is_better = False
if self.do_long and score_long <= self.hm[best_key_long]["long"][
"score"]:
is_better = True
logging.info(
f"i{cfg['config_no']} - new best config long, score {score_long:.7f} "
+
f"adg {adg_mean_long / cfg['long']['wallet_exposure_limit']:.7f} "
+ f"PAD mean {PAD_mean_long_raw:.7f} " +
f"PAD std {PAD_std_long_raw:.5f} adg/DGstd {adg_DGstd_ratios_long_mean:.7f}"
)
tmp_fname += "_long"
json.dump(
results,
open(
f"{self.results_fpath}{cfg['config_no']:06}_result_long.json",
"w"),
indent=4,
sort_keys=True,
)
if self.do_short and score_short <= self.hm[best_key_short][
"short"]["score"]:
is_better = True
logging.info(
f"i{cfg['config_no']} - new best config short, score {score_short:.7f} "
+
f"adg {adg_mean_short / cfg['short']['wallet_exposure_limit']:.7f} "
+ f"PAD mean {PAD_mean_short_raw:.7f} " +
f"PAD std {PAD_std_short_raw:.5f} adg/DGstd {adg_DGstd_ratios_short_mean:.7f}"
)
tmp_fname += "_short"
json.dump(
results,
open(
f"{self.results_fpath}{cfg['config_no']:06}_result_short.json",
"w"),
indent=4,
sort_keys=True,
)
if is_better:
dump_live_config(best_config, tmp_fname + ".json")
elif cfg["config_no"] % 25 == 0:
logging.info(f"i{cfg['config_no']}")
results["config_no"] = cfg["config_no"]
with open(self.results_fpath + "all_results.txt", "a") as f:
f.write(
json.dumps({
"config": {
"long": cfg["long"],
"short": cfg["short"]
},
"results": results
}) + "\n")
del self.unfinished_evals[id_key]
self.workers[wi] = None
