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# src/ms/controller.py | |
tickers: List[Constituent] = convert_csv_to_records( | |
"data/tickers.csv", Constituent) | |
dt_to_milli = lambda dt: datetime.timestamp(dt) * 1000 | |
start = dt_to_milli(datetime(2018, 1, 1)) | |
end = dt_to_milli(datetime(2020, 1, 1)) | |
extract_n_store_cup_with_handles(start, end, tickers) |
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Start date 2016-01-04 | |
End date 2017-12-29 | |
Total months 23 | |
Backtest | |
--------- | |
Annual return 9.7% | |
Cumulative returns 20.2% | |
Annual volatility 7.5% | |
Sharpe ratio 1.27 |
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WATCHLIST_WINDOW_DAYS = 30 | |
ABOVE_PIVOT_PCT = 1.01 | |
TAKE_PROFIT_PCT = 1.15 | |
STOP_LOSS_PCT = .95 | |
PATIENCE_WINDOW_DAYS = 21 | |
START = datetime(2016, 1, 1) | |
END = datetime(2018, 1, 1) | |
BENCHMARK = "SPY" | |
SHORT_MA_LEN = 50 | |
LONG_MA_LEN = 200 |
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# format start end | |
to_localized_ts = lambda dt: pd.Timestamp(dt).tz_localize("UTC") | |
start, end = to_localized_ts(START), to_localized_ts(END) | |
# get returns | |
benchmark = get_benchmark_returns() | |
# run strat | |
results = zp.run_algorithm( | |
start=start, |
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def analyze(perf: pd.DataFrame, bench: pd.Series) -> None: | |
returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(perf) | |
pf.create_full_tear_sheet(returns=returns, benchmark_rets=bench) |
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def get_benchmark_returns() -> pd.Series: | |
bench = yf.Ticker(BENCHMARK) | |
bench_hist = bench_hist.history(start=START, end=END, auto_adjust=True).tz_localize("UTC") | |
returns = pd.Series(bench_hist["Close"].pct_change().values, index=bench_hist.index).dropna() | |
returns.index.names = ["date"] | |
return returns |
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def handle_data(context, data): | |
current_dt = zp.api.get_datetime() | |
prices = data.history(context.stocks, "price", bar_count=200, frequency="1d") | |
# look for new trades | |
for ix, pattern in context.patterns.iterrows(): | |
# skip if asset is already in portfolio | |
open_positions = set(context.portfolio.positions.keys()) | |
symbol = zp.api.symbol(pattern["symbol"]) |
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def initialize(context): | |
# avoid out of bounds error by dropping firstBottomDate col | |
patterns = pd.read_csv("data/patterns.csv").drop(["firstBottomDate"], axis=1) | |
patterns = convert_date_cols(patterns) | |
context.patterns = patterns | |
tickers = pd.read_csv("data/tickers.csv") | |
tickers = convert_date_cols(tickers) | |
context.stocks = [zp.api.symbol(ticker) for ticker in tickers.symbol] |
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def convert_date_cols(df: pd.DataFrame) -> pd.DataFrame: | |
"""Given a dataframe, adds UTC timezone to all columns that have date in their names.""" | |
for col in df.columns: | |
if("date" in col.lower()): | |
df[col] = pd.to_datetime(df[col]).dt.tz_localize("UTC") | |
return df |
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WATCHLIST_WINDOW_DAYS = 30 | |
ABOVE_PIVOT_PCT = 1.01 | |
TAKE_PROFIT_PCT = 1.15 | |
STOP_LOSS_PCT = .95 | |
PATIENCE_WINDOW_DAYS = 21 | |
START = datetime(2016, 1, 1) | |
END = datetime(2018, 1, 1) | |
BENCHMARK = "SPY" | |
SHORT_MA_LEN = 50 | |
LONG_MA_LEN = 200 |
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