def get_historical_data_from_poylgon_for_symbols(
api: tradeapi, symbols: List[str], start_date: date, end_date: date
) -> Dict[str, df]:
minute_history = {}
for symbol in symbols:
if symbol not in minute_history:
minute_history[symbol] = api.polygon.historic_agg_v2(
symbol,
1,
"minute",
_from=start_date,
to=end_date,
).df.tz_convert("US/Eastern")
add_daily_vwap(minute_history[symbol])
return minute_history
async def backtest_symbol(
symbol: str, scanner_start_time: datetime
) -> None:
est = pytz.timezone("America/New_York")
scanner_start_time = (
pytz.utc.localize(scanner_start_time).astimezone(est)
if scanner_start_time.tzinfo is None
else scanner_start_time
)
start_time = pytz.utc.localize(start).astimezone(est)
if scanner_start_time > start_time + duration:
print(
f"{symbol} picked too late at {scanner_start_time} ({start_time}, {duration})"
)
return
start_time = scanner_start_time
if start_time.second > 0:
start_time = start_time.replace(second=0, microsecond=0)
print(
f"--> back-testing {symbol} from {str(start_time)} duration {duration}"
)
if debug_symbols and symbol in debug_symbols:
print("--> using DEBUG mode")
re_try = 3
while re_try > 0:
# load historical data
try:
symbol_data = data_api.polygon.historic_agg_v2(
symbol,
1,
"minute",
_from=str(start_time - timedelta(days=8)),
to=str(start_time + timedelta(days=1)),
limit=10000,
).df
except HTTPError as e:
tlog(f"Received HTTP error {e} for {symbol}")
return
if len(symbol_data) < 100:
tlog(f"not enough data-points for {symbol}")
return
add_daily_vwap(
symbol_data,
debug=debug_symbols and symbol in debug_symbols,
)
market_data.minute_history[symbol] = symbol_data
print(
f"loaded {len(market_data.minute_history[symbol].index)} agg data points"
)
position: int = 0
try:
minute_index = symbol_data["close"].index.get_loc(
start_time, method="nearest"
)
break
except (Exception, ValueError) as e:
print(f"[EXCEPTION] {e} - trying to reload-data. ")
re_try -= 1
new_now = symbol_data.index[minute_index]
print(f"start time with data {new_now}")
price = 0.0
last_run_id = None
# start_time + duration
while (
new_now < config.market_close
and minute_index < symbol_data.index.size - 1
):
if symbol_data.index[minute_index] != new_now:
print(
"mismatch!", symbol_data.index[minute_index], new_now
)
print(
symbol_data["close"][
minute_index - 10 : minute_index + 1
]
)
raise Exception()
price = symbol_data["close"][minute_index]
for strategy in trading_data.strategies:
if debug_symbols and symbol in debug_symbols:
print(
f"Execute strategy {strategy.name} on {symbol} at {new_now}"
)
do, what = await strategy.run(
symbol,
True,
position,
symbol_data[: minute_index + 1],
new_now,
portfolio_value,
debug=debug_symbols and symbol in debug_symbols, # type: ignore
backtesting=True,
)
if do:
if (
what["side"] == "buy"
and float(what["qty"]) > 0
or what["side"] == "sell"
and float(what["qty"]) < 0
):
position += int(float(what["qty"]))
trading_data.buy_time[symbol] = new_now.replace(
second=0, microsecond=0
)
else:
position -= int(float(what["qty"]))
trading_data.last_used_strategy[symbol] = strategy
db_trade = NewTrade(
algo_run_id=strategy.algo_run.run_id,
symbol=symbol,
qty=int(float(what["qty"])),
operation=what["side"],
price=price,
indicators=trading_data.buy_indicators[symbol]
if what["side"] == "buy"
else trading_data.sell_indicators[symbol],
)
await db_trade.save(
config.db_conn_pool,
str(new_now),
trading_data.stop_prices[symbol],
trading_data.target_prices[symbol],
)
if what["side"] == "buy":
await strategy.buy_callback(
symbol, price, int(float(what["qty"]))
)
break
elif what["side"] == "sell":
await strategy.sell_callback(
symbol, price, int(float(what["qty"]))
)
break
last_run_id = strategy.algo_run.run_id
minute_index += 1
new_now = symbol_data.index[minute_index]
if position:
if (
trading_data.last_used_strategy[symbol].type
== StrategyType.DAY_TRADE
):
tlog(
f"[{new_now}]{symbol} liquidate {position} at {price}"
)
db_trade = NewTrade(
algo_run_id=last_run_id, # type: ignore
symbol=symbol,
qty=int(position)
if int(position) > 0
else -int(position),
operation="sell" if position > 0 else "buy",
price=price,
indicators={"liquidate": 1},
)
await db_trade.save(
config.db_conn_pool,
str(symbol_data.index[minute_index - 1]),
)
async def run(
self,
symbol: str,
shortable: bool,
position: int,
minute_history: df,
now: datetime,
portfolio_value: float = None,
trading_api: tradeapi = None,
debug: bool = False,
backtesting: bool = False,
) -> Tuple[bool, Dict]:
if not shortable:
return False, {}
data = minute_history.iloc[-1]
if data.close > data.average:
self.was_above_vwap[symbol] = True
if (await super().is_buy_time(now) and not position
and not open_orders.get(symbol, None)
and self.was_above_vwap.get(symbol, False)
and not self.traded.get(symbol, False)):
# Check for buy signals
lbound = config.market_open.replace(second=0, microsecond=0)
close = (minute_history["close"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
open = (minute_history["open"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").first()).dropna()
high = (minute_history["high"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").max()).dropna()
low = (minute_history["low"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").min()).dropna()
volume = (minute_history["volume"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").sum()).dropna()
volume = volume[volume != 0]
df = concat(
[
open.rename("open"),
high.rename("high"),
low.rename("low"),
close.rename("close"),
volume.rename("volume"),
],
axis=1,
)
if not add_daily_vwap(df):
tlog(f"[{now}]{symbol} failed in add_daily_vwap")
return False, {}
vwap_series = df["average"]
# calc macd on 5 min
close_5min = (minute_history["close"].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
if debug:
tlog(
f"\n{tabulate(df[-10:], headers='keys', tablefmt='psql')}")
macds = MACD(close_5min)
macd = macds[0].round(3)
macd_signal = macds[1].round(3)
macd_hist = macds[2].round(3)
vwap_series = vwap_series.round(3)
close = close.round(3)
to_buy = False
if (not self.potential_trap.get(symbol, False)
and close[-1] < vwap_series[-1]
and close[-2] < vwap_series[-2]
and close[-3] < vwap_series[-3] and close[-1] < open[-1]
and close[-2] < open[-2] and close[-3] < open[-3]
and macd[-1] < macd_signal[-1] < 0 and macd[-1] < 0
and macd_hist[-1] < macd_hist[-2] < macd_hist[-3] < 0 and
minute_history["close"][-2] < minute_history["open"][-2]
and minute_history["close"][lbound] <
minute_history["close"][-2] < minute_history["close"][-3] <
minute_history["close"][-4]):
# if data.close < high_2m:
# return False, {}
self.potential_trap[symbol] = True
self.trap_start_time[symbol] = now
tlog(
f"[{self.name}]:{symbol}@{now} potential short-trap {data.close}"
)
return False, {}
elif self.potential_trap.get(symbol, False):
a_vwap = anchored_vwap(minute_history,
self.trap_start_time[symbol])
if (len(a_vwap) > 10 and minute_history.close[-1] > a_vwap[-1]
and minute_history.close[-2] > a_vwap[-2]):
tlog(
f"[{self.name}]:{symbol}@{now} crossed above anchored-vwap {data.close}"
)
slope_min, _ = get_series_trend(minute_history.close[-10:])
slope_a_vwap, _ = get_series_trend(a_vwap[-10:])
if round(slope_min, 2) > round(slope_a_vwap, 2):
tlog(
f"[{self.name}]:{symbol}@{now} symbol slop {slope_min} above anchored-vwap slope {slope_a_vwap}"
)
else:
tlog(
f"[{self.name}]:{symbol}@{now} anchored-vwap slope {slope_a_vwap} below symbol {slope_min}"
)
return False, {}
stop_price = data.close * 0.99 # a_vwap[-1] * 0.99
target_price = stop_price * 1.1
stop_prices[symbol] = round(stop_price, 2)
target_prices[symbol] = round(target_price, 2)
if portfolio_value is None:
if trading_api:
retry = 3
while retry > 0:
try:
portfolio_value = float(
trading_api.get_account(
).portfolio_value)
break
except ConnectionError as e:
tlog(
f"[{symbol}][{now}[Error] get_account() failed w/ {e}, retrying {retry} more times"
)
await asyncio.sleep(0)
retry -= 1
if not portfolio_value:
tlog(
"f[{symbol}][{now}[Error] failed to get portfolio_value"
)
return False, {}
else:
raise Exception(
f"{self.name}: both portfolio_value and trading_api can't be None"
)
shares_to_buy = int(portfolio_value * 0.02 / data.close)
if not shares_to_buy:
shares_to_buy = 1
buy_price = data.close
tlog(
f"[{self.name}][{now}] Submitting buy for {shares_to_buy} shares of {symbol} at {buy_price} target {target_prices[symbol]} stop {stop_prices[symbol]}"
)
buy_indicators[symbol] = {
"vwap_series":
vwap_series[-5:].tolist(),
"a_vwap_series":
a_vwap[-5:].tolist(),
"5-min-close":
close[-5:].tolist(),
"vwap":
data.vwap,
"avg":
data.average,
"volume":
minute_history["volume"][-5:].tolist(),
"slope_min":
slope_min,
"slope_a_vwap":
slope_a_vwap,
"volume_mean":
minute_history["volume"][lbound:].mean(),
"volume_std":
minute_history["volume"][lbound:].std(ddof=0, ),
}
self.buy_time[symbol] = now
return (
True,
{
"side": "buy",
"qty": str(shares_to_buy),
"type": "limit",
"limit_price": str(buy_price),
},
)
if (await super().is_sell_time(now) and position
and last_used_strategy[symbol].name == self.name
and not open_orders.get(symbol)):
rsi = RSI(
minute_history["close"].dropna().between_time("9:30", "16:00"),
14,
)
a_vwap = anchored_vwap(minute_history, self.buy_time[symbol])
sell_reasons = []
to_sell = False
if data.close <= stop_prices[symbol]:
to_sell = True
sell_reasons.append("stopped")
elif data.close >= target_prices[symbol]:
to_sell = True
sell_reasons.append("above target")
elif rsi[-1] > 79 and data.close > latest_cost_basis[symbol]:
to_sell = True
sell_reasons.append("RSI maxed")
elif round(data.vwap, 2) >= round(data.average, 2):
# to_sell = True
sell_reasons.append("crossed above vwap")
elif round(data.vwap, 2) < a_vwap[-1] * 0.99:
to_sell = True
sell_reasons.append("crossed below a-vwap")
if to_sell:
sell_indicators[symbol] = {
"vwap": data.vwap,
"avg": data.average,
"reasons": sell_reasons,
"rsi": rsi[-5:].round(2).tolist(),
"a_vwap": a_vwap[-5:].round(2).tolist(),
}
tlog(
f"[{self.name}][{now}] Submitting sell for {position} shares of {symbol} at market with reason:{sell_reasons}"
)
return (
True,
{
"side": "sell",
"qty": str(position),
"type": "market",
},
)
return False, {}
async def backtest_symbol(
data_loader: DataLoader,
portfolio_value: float,
symbol: str,
start: datetime,
duration: timedelta,
scanner_start_time: datetime,
debug_symbol: bool = False,
) -> None:
est = pytz.timezone("America/New_York")
scanner_start_time = (pytz.utc.localize(scanner_start_time).astimezone(est)
if scanner_start_time.tzinfo is None else
scanner_start_time)
start_time = pytz.utc.localize(start).astimezone(est)
if scanner_start_time > start_time + duration:
print(
f"{symbol} picked too late at {scanner_start_time} ({start_time}, {duration})"
)
return
start_time = scanner_start_time
if start_time.second > 0:
start_time = start_time.replace(second=0, microsecond=0)
print(
f"--> back-testing {symbol} from {str(start_time)} duration {duration}"
)
if debug_symbol:
print("--> using DEBUG mode")
symbol_data = pd.DataFrame(data_loader[symbol][start_time:start_time +
duration] # type: ignore
)
add_daily_vwap(
symbol_data,
debug=debug_symbol,
)
print(
f"loaded {len(symbol_data)} agg data points({start_time}-{start_time + duration})"
)
minute_index = symbol_data["close"].index.get_loc(start_time,
method="nearest")
position: int = 0
new_now = symbol_data.index[minute_index]
print(f"start time with data {new_now}")
price = 0.0
last_run_id = None
# start_time + duration
rejected: Dict[str, List] = {}
while (new_now < config.market_close
and minute_index < symbol_data.index.size - 1):
if symbol_data.index[minute_index] != new_now:
print("mismatch!", symbol_data.index[minute_index], new_now)
print(symbol_data["close"][minute_index - 10:minute_index + 1])
raise Exception()
price = symbol_data["close"][minute_index]
for strategy in trading_data.strategies:
if debug_symbol:
print(
f"Execute strategy {strategy.name} on {symbol} at {new_now}"
)
if symbol in rejected.get(strategy.name, []):
continue
try:
do, what = await strategy.run(
symbol,
True,
position,
symbol_data[:minute_index + 1],
new_now,
portfolio_value,
debug=debug_symbol, # type: ignore
backtesting=True,
)
except Exception as e:
traceback.print_exc()
tlog(
f"[ERROR] exception {e} on symbol {symbol} @ {strategy.name}"
)
continue
if do:
if (what["side"] == "buy" and float(what["qty"]) > 0
or what["side"] == "sell" and float(what["qty"]) < 0):
position += int(float(what["qty"]))
trading_data.buy_time[symbol] = new_now.replace(
second=0, microsecond=0)
else:
position -= int(float(what["qty"]))
trading_data.last_used_strategy[symbol] = strategy
db_trade = NewTrade(
algo_run_id=strategy.algo_run.run_id,
symbol=symbol,
qty=int(float(what["qty"])),
operation=what["side"],
price=price,
indicators=trading_data.buy_indicators[symbol]
if what["side"] == "buy" else
trading_data.sell_indicators[symbol],
)
await db_trade.save(
config.db_conn_pool,
str(new_now),
trading_data.stop_prices[symbol],
trading_data.target_prices[symbol],
)
if what["side"] == "buy":
await strategy.buy_callback(symbol, price,
int(float(what["qty"])))
break
elif what["side"] == "sell":
await strategy.sell_callback(symbol, price,
int(float(what["qty"])))
break
elif what.get("reject", False):
if strategy.name in rejected:
rejected[strategy.name].append(symbol)
else:
rejected[strategy.name] = [symbol]
last_run_id = strategy.algo_run.run_id
minute_index += 1
new_now = symbol_data.index[minute_index]
if position:
if (trading_data.last_used_strategy[symbol].type ==
StrategyType.DAY_TRADE):
tlog(f"[{new_now}]{symbol} liquidate {position} at {price}")
db_trade = NewTrade(
algo_run_id=last_run_id, # type: ignore
symbol=symbol,
qty=int(position) if int(position) > 0 else -int(position),
operation="sell" if position > 0 else "buy",
price=price,
indicators={"liquidate": 1},
)
await db_trade.save(
config.db_conn_pool,
str(symbol_data.index[minute_index - 1]),
)
async def update_vwap(self, symbol: str, now: datetime) -> None:
if not self.data_loader[symbol][now].average:
add_daily_vwap(self.data_loader[symbol])
async def run(
self,
symbol: str,
position: int,
minute_history: df,
now: datetime,
portfolio_value: float = None,
trading_api: tradeapi = None,
debug: bool = False,
backtesting: bool = False,
) -> Tuple[bool, Dict]:
data = minute_history.iloc[-1]
prev_minute = minute_history.iloc[-2]
prev_2minutes = minute_history.iloc[-3]
if await self.is_buy_time(now) and not position:
# Check for buy signals
back_time = ts(config.market_open)
back_time_index = minute_history["close"].index.get_loc(
back_time, method="nearest")
close = (minute_history["close"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
open = (minute_history["open"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").first()).dropna()
high = (minute_history["high"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").max()).dropna()
low = (minute_history["low"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").min()).dropna()
volume = (minute_history["volume"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").sum()).dropna()
_df = concat(
[
open.rename("open"),
high.rename("high"),
low.rename("low"),
close.rename("close"),
volume.rename("volume"),
],
axis=1,
)
if not add_daily_vwap(_df):
tlog(f"[{now}]failed add_daily_vwap")
return False, {}
if debug:
tlog(
f"\n[{now}]{symbol} {tabulate(_df[-10:], headers='keys', tablefmt='psql')}"
)
vwap_series = _df["average"]
if debug:
tlog(
f"[{now}] {symbol} close:{round(data.close,2)} vwap:{round(vwap_series[-1],2)}"
)
if len(vwap_series) < 3:
tlog(f"[{now}]{symbol}: missing vwap values {vwap_series}")
return False, {}
if close[-2] > vwap_series[-2] and close[-1] < vwap_series[-1]:
down_cross[symbol] = vwap_series.index[-1].to_pydatetime()
tlog(
f"[{now}] {symbol} down-crossing on 5-min bars at {down_cross[symbol]}"
)
return False, {}
if (close[-2] > vwap_series[-2] and close[-3] < vwap_series[-3]
and data.close > prev_minute.close
and data.close > data.average):
if not symbol in down_cross:
tlog(
f"[{now}] {symbol} did not find download crossing in the past 15 min"
)
return False, {}
if minute_history.index[-1].to_pydatetime(
) - down_cross[symbol] > timedelta(minutes=30):
tlog(
f"[{now}] {symbol} down-crossing too far {down_cross[symbol]} from now"
)
return False, {}
stop_price = find_stop(
data.close if not data.vwap else data.vwap,
minute_history,
now,
)
target = data.close + 0.05
stop_prices[symbol] = stop_price
target_prices[symbol] = target
tlog(
f"{symbol} found conditions for VWAP-Scalp strategy now:{now}"
)
tlog(
f"\n{tabulate(minute_history[-10:], headers='keys', tablefmt='psql')}"
)
if portfolio_value is None:
if trading_api:
portfolio_value = float(
trading_api.get_account().portfolio_value)
else:
raise Exception(
"VWAPLong.run(): both portfolio_value and trading_api can't be None"
)
shares_to_buy = (
portfolio_value * 20.0 * config.risk // data.close
# // (data.close - stop_prices[symbol])
)
if not shares_to_buy:
shares_to_buy = 1
shares_to_buy -= position
if shares_to_buy > 0:
tlog(
f"[{self.name}] Submitting buy for {shares_to_buy} shares of {symbol} at {data.close} target {target_prices[symbol]} stop {stop_prices[symbol]}"
)
buy_indicators[symbol] = {
"average": round(data.average, 2),
"vwap": round(data.vwap, 2),
"patterns": None,
}
return (
True,
{
"side": "buy",
"qty": str(shares_to_buy),
"type": "limit",
"limit_price": str(data.close + 0.01),
},
)
elif (await super().is_sell_time(now) and position > 0
and symbol in latest_cost_basis
and last_used_strategy[symbol].name == self.name):
if open_orders.get(symbol) is not None:
tlog(f"vwap_scalp: open order for {symbol} exists, skipping")
return False, {}
to_sell = False
to_sell_market = False
if data.vwap <= data.average - 0.05:
to_sell = True
reason = "below VWAP"
to_sell_market = True
elif data.close >= target_prices[symbol]:
to_sell = True
reason = "vwap scalp"
elif data.close >= target_prices[symbol]:
to_sell = True
reason = "vwap scalp"
elif (prev_minute.close < prev_minute.open
and data.close < data.open):
to_sell = True
reason = "vwap scalp no bears"
if to_sell:
sell_indicators[symbol] = {
"reason": reason,
"average": data.average,
"vwap": data.vwap,
}
return (
True,
{
"side": "sell",
"qty": str(position),
"type": "market"
} if to_sell_market else {
"side": "sell",
"qty": str(position),
"type": "limit",
"limit_price": str(data.close),
},
)
return False, {}
async def run(
self,
symbol: str,
shortable: bool,
position: int,
minute_history: df,
now: datetime,
portfolio_value: float = None,
trading_api: tradeapi = None,
debug: bool = False,
backtesting: bool = False,
) -> Tuple[bool, Dict]:
if not shortable:
return False, {}
data = minute_history.iloc[-1]
if data.close > data.average:
self.was_above_vwap[symbol] = True
if (await super().is_buy_time(now) and not position
and not open_orders.get(symbol, None)):
day_start = ts(config.market_open)
try:
day_start_index = minute_history["close"].index.get_loc(
day_start, method="nearest")
except Exception as e:
tlog(
f"[ERROR]{self.name}[{now}]{symbol} can't load index for {day_start} w/ {e}"
)
return False, {}
close = (minute_history["close"]
[day_start_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
open = (minute_history["open"]
[day_start_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").first()).dropna()
high = (minute_history["high"]
[day_start_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").max()).dropna()
low = (minute_history["low"]
[day_start_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").min()).dropna()
volume = (minute_history["volume"]
[day_start_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").sum()).dropna()
df = concat(
[
open.rename("open"),
high.rename("high"),
low.rename("low"),
close.rename("close"),
volume.rename("volume"),
],
axis=1,
)
if not add_daily_vwap(df):
tlog(f"[{now}]{symbol} failed in add_daily_vwap")
return False, {}
vwap_series = df["average"]
if (data.close < vwap_series[-1] * 0.99
and self.was_above_vwap.get(symbol, False) and close[-1] <
open[-1] <= close[-2] < open[-2] <= close[-3] < open[-3]):
stop_price = vwap_series[-1]
target_price = data.close - 3 * (stop_price - data.close)
stop_prices[symbol] = stop_price
target_prices[symbol] = target_price
if portfolio_value is None:
if trading_api:
retry = 3
while retry > 0:
try:
portfolio_value = float(
trading_api.get_account().portfolio_value)
break
except ConnectionError as e:
tlog(
f"[{symbol}][{now}[Error] get_account() failed w/ {e}, retrying {retry} more times"
)
await asyncio.sleep(0)
retry -= 1
if not portfolio_value:
tlog(
"f[{symbol}][{now}[Error] failed to get portfolio_value"
)
return False, {}
else:
raise Exception(
f"{self.name}: both portfolio_value and trading_api can't be None"
)
shares_to_buy = (portfolio_value * config.risk //
(data.close - stop_prices[symbol]))
if not shares_to_buy:
shares_to_buy = 1
buy_price = data.close
tlog(
f"[{self.name}][{now}] Submitting buy short for {-shares_to_buy} shares of {symbol} at {buy_price} target {target_prices[symbol]} stop {stop_prices[symbol]}"
)
sell_indicators[symbol] = {
"vwap_series": vwap_series[-5:].tolist(),
"vwap": data.vwap,
"avg": data.average,
}
return (
True,
{
"side": "sell",
"qty": str(-shares_to_buy),
"type": "market",
},
)
if (await super().is_sell_time(now) and position
and last_used_strategy[symbol].name == self.name
and not open_orders.get(symbol)):
day_start = ts(config.market_open)
day_start_index = minute_history["close"].index.get_loc(
day_start, method="nearest")
close = (minute_history["close"]
[day_start_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
to_sell: bool = False
reason: str = ""
if data.close >= stop_prices[symbol]:
to_sell = True
reason = "stopped"
elif data.close <= target_prices[symbol]:
to_sell = True
reason = "target reached"
elif close[-1] > close[-2] > close[-3] < close[-4]:
to_sell = True
reason = "reversing direction"
if to_sell:
buy_indicators[symbol] = {
"close_5m": close[-5:].tolist(),
"reason": reason,
}
tlog(
f"[{self.name}][{now}] Submitting sell short for {position} shares of {symbol} at market {data.close} with reason:{reason}"
)
return (
True,
{
"side": "buy",
"qty": str(-position),
"type": "market",
},
)
return False, {}
async def run(
self,
symbol: str,
position: int,
minute_history: df,
now: datetime,
portfolio_value: float = None,
trading_api: tradeapi = None,
debug: bool = False,
backtesting: bool = False,
) -> Tuple[bool, Dict]:
data = minute_history.iloc[-1]
prev_minute = minute_history.iloc[-2]
if await self.is_buy_time(now) and not position:
# Check for buy signals
lbound = config.market_open
ubound = lbound + timedelta(minutes=15)
try:
high_15m = minute_history[lbound:ubound]["high"].max(
) # type: ignore
if data.vwap < high_15m:
return False, {}
except Exception as e:
# Because we're aggregating on the fly, sometimes the datetime
# index can get messy until it's healed by the minute bars
tlog(
f"[{self.name}] error aggregation {e} - maybe should use nearest?"
)
return False, {}
back_time = ts(config.market_open)
back_time_index = minute_history["close"].index.get_loc(
back_time, method="nearest")
close = (minute_history["close"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
open = (minute_history["open"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").first()).dropna()
high = (minute_history["high"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").max()).dropna()
low = (minute_history["low"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").min()).dropna()
volume = (minute_history["volume"]
[back_time_index:-1].dropna().between_time(
"9:30", "16:00").resample("5min").sum()).dropna()
_df = concat(
[
open.rename("open"),
high.rename("high"),
low.rename("low"),
close.rename("close"),
volume.rename("volume"),
],
axis=1,
)
if not add_daily_vwap(_df):
tlog(f"[{now}]failed add_daily_vwap")
return False, {}
if debug:
tlog(
f"\n[{now}]{symbol} {tabulate(_df[-10:], headers='keys', tablefmt='psql')}"
)
vwap_series = _df["average"]
if (
# data.vwap > close_series[-1] > close_series[-2]
# and round(data.average, 2) > round(vwap_series[-1], 2)
# and data.vwap > data.average
# and
data.low > data.average
and close[-1] > vwap_series[-1] > vwap_series[-2] > low[-2]
and close[-1] > high[-2]
and prev_minute.close > prev_minute.open
and data.close > data.open
and low[-2] < vwap_series[-2] - 0.2):
stop_price = find_stop(
data.close if not data.vwap else data.vwap,
minute_history,
now,
)
# upperband, middleband, lowerband = BBANDS(
# minute_history["close"], timeperiod=20
# )
# stop_price = min(
# prev_minute.close,
# data.average - 0.01,
# lowerband[-1] - 0.03,
# )
target = (3 * (data.close - stop_price) + data.close
) # upperband[-1]
# if target - stop_price < 0.05:
# tlog(
# f"{symbol} target price {target} too close to stop price {stop_price}"
# )
# return False, {}
# if target - data.close < 0.05:
# tlog(
# f"{symbol} target price {target} too close to close price {data.close}"
# )
# return False, {}
stop_prices[symbol] = stop_price
target_prices[symbol] = target
patterns: Dict[ts, Dict[int, List[str]]] = {}
pattern_functions = talib.get_function_groups(
)["Pattern Recognition"]
for pattern in pattern_functions:
pattern_value = getattr(talib, pattern)(open, high, low,
close)
result = pattern_value.to_numpy().nonzero()
if result[0].size > 0:
for timestamp, value in pattern_value.iloc[
result].items():
t = ts(timestamp)
if t not in patterns:
patterns[t] = {}
if value not in patterns[t]:
patterns[t][value] = [pattern]
else:
patterns[t][value].append(pattern)
tlog(f"{symbol} found conditions for VWAP strategy now:{now}")
candle_s = Series(patterns)
candle_s = candle_s.sort_index()
tlog(f"{symbol} 5-min VWAP {vwap_series}")
tlog(f"{symbol} 5-min close values {close}")
tlog(f"{symbol} {candle_s}")
tlog(
f"\n{tabulate(minute_history[-10:], headers='keys', tablefmt='psql')}"
)
if candle_s.size > 0 and -100 in candle_s[-1]:
tlog(
f"{symbol} Bullish pattern does not exists -> should skip"
)
# return False, {}
if portfolio_value is None:
if trading_api:
portfolio_value = float(
trading_api.get_account().portfolio_value)
else:
raise Exception(
"VWAPLong.run(): both portfolio_value and trading_api can't be None"
)
shares_to_buy = (
portfolio_value * 20.0 * config.risk // data.close
# // (data.close - stop_prices[symbol])
)
print(
f"shares to buy {shares_to_buy} {data.close} {stop_prices[symbol]}"
)
if not shares_to_buy:
shares_to_buy = 1
shares_to_buy -= position
if shares_to_buy > 0:
tlog(
f"[{self.name}] Submitting buy for {shares_to_buy} shares of {symbol} at {data.close} target {target_prices[symbol]} stop {stop_prices[symbol]}"
)
buy_indicators[symbol] = {
# bbrand_lower": lowerband[-5:].tolist(),
# "bbrand_middle": middleband[-5:].tolist(),
# "bbrand_upper": upperband[-5:].tolist(),
"average": round(data.average, 2),
"vwap": round(data.vwap, 2),
"patterns": candle_s.to_json(),
}
return (
True,
{
"side": "buy",
"qty": str(shares_to_buy),
"type": "limit",
"limit_price": str(data.close),
},
)
elif debug:
tlog(f"[{now}]{symbol} failed vwap strategy")
if not (data.low > data.average):
tlog(
f"[{now}]{symbol} failed data.low {data.low} > data.average {data.average}"
)
if not (close[-1] > vwap_series[-1] > vwap_series[-2] >
low[-2]):
tlog(
f"[{now}]{symbol} failed close[-1] {close[-1]} > vwap_series[-1] {vwap_series[-1]} > vwap_series[-2]{ vwap_series[-2]} > low[-2] {low[-2]}"
)
if not (prev_minute.close > prev_minute.open):
tlog(
f"[{now}]{symbol} failed prev_minute.close {prev_minute.close} > prev_minute.open {prev_minute.open}"
)
if not (close[-1] > high[-2]):
tlog(
f"[{now}]{symbol} failed close[-1] {close[-1]} > high[-2] {high[-2]}"
)
if not (data.close > data.open):
tlog(
f"[{now}]{symbol} failed data.close {data.close} > data.open {data.open}"
)
if not low[-2] < vwap_series[-2] - 0.2:
tlog(
f"[{now}]{symbol} failed low[-2] {low[-2]} < vwap_series[-2] {vwap_series[-2] } - 0.2"
)
elif (await super().is_sell_time(now) and position > 0
and symbol in latest_cost_basis
and last_used_strategy[symbol].name == self.name):
if open_orders.get(symbol) is not None:
tlog(f"vwap_long: open order for {symbol} exists, skipping")
return False, {}
if data.vwap <= data.average - 0.02:
sell_indicators[symbol] = {
"reason": "below VWAP",
"average": data.average,
"vwap": data.vwap,
}
return (
True,
{
"side": "sell",
"qty": str(position),
"type": "market"
},
)
return False, {}
async def run(
self,
symbol: str,
shortable: bool,
position: int,
minute_history: df,
now: datetime,
portfolio_value: float = None,
trading_api: tradeapi = None,
debug: bool = False,
backtesting: bool = False,
) -> Tuple[bool, Dict]:
if not shortable:
return False, {}
data = minute_history.iloc[-1]
if data.close > data.average:
self.was_above_vwap[symbol] = True
if (await super().is_buy_time(now) and not position
and not open_orders.get(symbol, None)):
if data.open > data.average:
if debug:
tlog(f"{self.name} {symbol} trending up: {data}")
return False, {}
lbound = config.market_open.replace(second=0, microsecond=0)
close = (minute_history["close"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
open = (minute_history["open"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").first()).dropna()
high = (minute_history["high"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").max()).dropna()
low = (minute_history["low"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").min()).dropna()
volume = (minute_history["volume"][lbound:].dropna().between_time(
"9:30", "16:00").resample("5min").sum()).dropna()
volume = volume[volume != 0]
df = concat(
[
open.rename("open"),
high.rename("high"),
low.rename("low"),
close.rename("close"),
volume.rename("volume"),
],
axis=1,
)
if not add_daily_vwap(df):
tlog(f"[{now}]{symbol} failed in add_daily_vwap")
return False, {}
vwap_series = df["average"]
# calc macd on 5 min
close_5min = (minute_history["close"].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
if debug:
tlog(
f"\n{tabulate(df[-10:], headers='keys', tablefmt='psql')}")
macds = MACD(close_5min)
macd = macds[0].round(3)
macd_signal = macds[1].round(3)
macd_hist = macds[2].round(3)
vwap_series = vwap_series.round(3)
close = close.round(3)
if (self.was_above_vwap.get(symbol, False)
and close[-1] < vwap_series[-1]
and close[-2] < vwap_series[-2]
and close[-3] < vwap_series[-3] and close[-1] < open[-1]
and close[-2] < open[-2] and close[-3] < open[-3]
and macd[-1] < macd_signal[-1] < 0
# and macd[-1] < 0
and macd_hist[-1] < macd_hist[-2] < macd_hist[-3] < 0 and
data.close < data.open and data.close <
minute_history["close"][-2] < minute_history["close"][-3]):
stops = find_supports(data.close, minute_history, now,
StopRangeType.LAST_2_HOURS)
if stops:
tlog(
f"[self.name]:{symbol}@{data.close} potential short-trap {stops}"
)
return False, {}
tlog(
f"\n{tabulate(df[-10:], headers='keys', tablefmt='psql')}")
stop_price = vwap_series[-1] * 1.005
target_price = round(
min(
data.close - 10 * (stop_price - data.close),
data.close * 0.98,
),
2,
)
stop_prices[symbol] = round(stop_price, 2)
target_prices[symbol] = round(target_price, 2)
if portfolio_value is None:
if trading_api:
retry = 3
while retry > 0:
try:
portfolio_value = float(
trading_api.get_account().portfolio_value)
break
except ConnectionError as e:
tlog(
f"[{symbol}][{now}[Error] get_account() failed w/ {e}, retrying {retry} more times"
)
await asyncio.sleep(0)
retry -= 1
if not portfolio_value:
tlog(
"f[{symbol}][{now}[Error] failed to get portfolio_value"
)
return False, {}
else:
raise Exception(
f"{self.name}: both portfolio_value and trading_api can't be None"
)
shares_to_buy = int(
max(
portfolio_value * config.risk //
(data.close - stop_prices[symbol]),
-portfolio_value * 0.02 // data.close,
))
if not shares_to_buy:
shares_to_buy = 1
buy_price = data.close
tlog(
f"[{self.name}][{now}] Submitting buy short for {-shares_to_buy} shares of {symbol} at {buy_price} target {target_prices[symbol]} stop {stop_prices[symbol]}"
)
sell_indicators[symbol] = {
"vwap_series": vwap_series[-5:].tolist(),
"5-min-close": close[-5:].tolist(),
"vwap": data.vwap,
"avg": data.average,
"volume": minute_history["volume"][-5:].tolist(),
"stops": [] if not stops else stops.tolist(),
}
self.volume_test_time[symbol] = now
return (
True,
{
"side": "sell",
"qty": str(-shares_to_buy),
"type": "market",
},
)
if (await super().is_sell_time(now) and position
and last_used_strategy[symbol].name == self.name
and not open_orders.get(symbol)):
volume = minute_history["volume"][
self.volume_test_time[symbol]:].dropna()
mu, std = norm.fit(volume)
a_vwap = anchored_vwap(minute_history,
self.volume_test_time[symbol])
close_5min = (minute_history["close"].dropna().between_time(
"9:30", "16:00").resample("5min").last()).dropna()
to_sell: bool = False
reason: str = ""
macds = MACD(close_5min, 13, 21)
macd = macds[0].round(2)
macd_signal = macds[1].round(2)
macd_hist = macds[2].round(2)
close_5min = close_5min.round(2)
movement = (data.close -
latest_cost_basis[symbol]) / latest_cost_basis[symbol]
if (data.close >= stop_prices[symbol]
and macd[-1] > macd_signal[-1]):
to_sell = True
reason = "stopped"
elif data.close <= target_prices[symbol]:
to_sell = True
reason = "target reached"
elif (close_5min[-1] > close_5min[-2] > close_5min[-3] <
close_5min[-4] and data.close < latest_cost_basis[symbol]):
to_sell = True
reason = "reversing direction"
elif (macd[-1] > macd_signal[-1]
and data.close < latest_cost_basis[symbol]):
to_sell = True
reason = "MACD changing trend"
elif (0 > macd_hist[-4] > macd_hist[-3] < macd_hist[-2] <
macd_hist[-1] < 0
and data.close < latest_cost_basis[symbol]):
to_sell = True
reason = "MACD histogram trend reversal"
elif (len(a_vwap) > 10 and minute_history.close[-1] > a_vwap[-2]
and minute_history.close[-2] > a_vwap[-2]):
slope_min, intercept_min, _, _, _ = linregress(
range(10), minute_history.close[-10:])
slope_a_vwap, intercept_a_vwap, _, _, _ = linregress(
range(10), a_vwap[-10:])
if round(slope_min, 2) > round(slope_a_vwap, 2):
to_sell = True
reason = f"deviate from anchored-vwap {round(slope_min, 2)}>{round(slope_a_vwap, 2)}"
# elif data.volume > mu + 2 * std and data.close > data.open and data.vwap > data.open:
# to_sell = True
# reason = "suspicious spike in volume, may be short-trap"
if to_sell:
buy_indicators[symbol] = {
"close_5m": close_5min[-5:].tolist(),
"movement": movement,
"reason": reason,
"volume": minute_history.volume[-5:].tolist(),
"volume fit": (mu, std),
}
tlog(
f"[{self.name}][{now}] Submitting sell short for {position} shares of {symbol} at market {data.close} with reason:{reason}"
)
return (
True,
{
"side": "buy",
"qty": str(-position),
"type": "market",
},
)
return False, {}
