Exportação de previsões de modelos para backtests de carteira no QuantConnect

Código Machine Learning for Trading
Resumo

Este notebook demonstra a separação entre um pipeline de aprendizado de máquina offline e o mecanismo LEAN do QuantConnect. Seleciona um conjunto de previsões por meio do registro de estudos de caso ETF, verifica a proveniência do treinamento e das previsões e exporta previsões de holdout fora da amostra em uma estrutura JSON que a plataforma pode consumir. O algoritmo da plataforma aplica regras de carteira a essas pontuações sem reproduzir o pipeline de inferência do modelo. Congelar as previsões também permite que pesquisadores testem limites, limites de posição e regras de rebalanceamento sem novo treinamento.
O notebook contrasta essa abordagem de pontuações pré-computadas com a inferência integrada. Previsões congeladas permitem experimentos reproduzíveis com regras de carteira, enquanto o uso ao vivo exige um processo que produza arquivos atualizados regularmente; a inferência integrada pode usar entradas atuais, mas depende da compatibilidade entre o modelo e o ambiente de execução. O exemplo usa hashes do registro e dos arquivos para verificar os artefatos selecionados e garantir que o registro não tenha mudado durante a exportação. A demonstração se limita a previsões de ETF e a um fluxo de trabalho em plataforma gerenciada; um backtest com previsões de holdout só é significativo se esse holdout não tiver sido usado na seleção do modelo e da configuração.
Ideias principais

Exporte as pontuações do modelo fora da amostra em um arquivo portátil para a plataforma consumir.
Mantenha as regras de construção da carteira e execução na plataforma, deixando a inferência do modelo no pipeline de pesquisa.
Use hashes de proveniência para verificar qual execução de treinamento e artefato de previsão produziram uma exportação.
Previsões congeladas agilizam experimentos com regras de carteira, pois alterá-las não exige novo treinamento.
A implantação ao vivo exige previsões atualizadas, enquanto a inferência integrada introduz dependências do modelo e do ambiente de execução.
Tags

Aprendizado de máquina Execução Backtesting Construção de portfólio Ações
Texto completo
# 06_quantconnect_case_study.py


```py
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# %% [markdown]
# # QuantConnect Deployment: Prediction Export Bridge
#
# **Docker image**: `ml4t`
#
# **Book Reference**: Chapter 25, Section 25.4 (QuantConnect and managed platforms)
#
# A managed platform will run a backtest and route orders, and will not run this book's feature
# pipeline. Reimplementing that pipeline inside the platform's language is a second
# implementation of the thing
# [`01_unified_framework_demo`](01_unified_framework_demo.ipynb) spent its whole length arguing
# against having.
#
# The way out is to move the boundary. The model stays here and its output crosses over: a file
# of predictions, one score per symbol per date, which the platform reads and turns into a
# portfolio. What runs on the platform is then a few dozen lines of position rules with no
# inference in it, and what changes when a threshold moves is a backtest rather than a retrain.
#
# The cost is that the platform can only trade dates the file covers, so a live deployment needs
# a job that keeps writing it. This notebook builds the export and the algorithm that consumes
# it, and states where each pattern gives out.
#
# **Learning Objectives**
# - Export a model's predictions into a file an external platform can read, with the provenance
#   needed to say which run produced it
# - Read a platform algorithm that holds portfolio rules and no inference
# - Say when precomputed predictions are the right boundary and when inline inference is
#
# **Prerequisites**: the ETFs case study, which produced the predictions exported here, and
# [`01_unified_framework_demo`](01_unified_framework_demo.ipynb) for the self-hosted
# alternative.

# %% [markdown]
# ## 1. Load Predictions from the ETFs Pipeline
#
# The ETFs case study's registry holds every training run, every prediction set and every
# backtest the case study produced. Exporting from it means first answering which of them to
# export, and doing that reproducibly.

# %%
"""Export ML predictions for consumption by LEAN algorithms."""

import hashlib
import json
import sqlite3

import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import polars as pl
from demo_artifacts import normalize_demo_predictions

from utils.paths import display_path, get_case_study_dir, get_output_dir, registry_readonly_uri
from utils.style import COLORS, FIGSIZE, add_message_title, show_with_alt

# %% [markdown]
# ## Settings
#
# `PREDICTION_THRESHOLD` is the predicted return above which a name is held long. Zero means
# every positive forecast is a candidate, which is the widest the portfolio can be, and the two
# figures below show what moving it does to breadth.
#
# The two pins are what make this export reproducible. `EXPECTED_TRAINING_HASH` names the
# configuration the registry query below is expected to select, and `EXPECTED_PREDICTIONS_SHA256`
# is the digest of the prediction file that configuration produced. Together they say: this run
# exported exactly the bytes that run exported.
#
# The registry file itself is deliberately not pinned. It accumulates a row for every run in the
# case study, so its digest moves whenever anything is added to it, whether or not the selection
# moves. A whole-file pin would therefore fail on runs that export exactly the right predictions,
# which is the worst kind of check: one that fires when nothing is wrong. Setting either pin to
# `None` reports the observed value instead of asserting it, which is what to do when the case
# study is deliberately re-promoted.

# %% tags=["parameters"]
PREDICTION_THRESHOLD = 0.0
EXPECTED_TRAINING_HASH = "ab5300c0cda4"
EXPECTED_PREDICTIONS_SHA256 = "2efad22dbf40464939d143745116165c6447c3e4dfd154bd608dda8653b52925"
EXPORT_PATH = get_output_dir(25, "quantconnect_export") / "ml4t_qc_predictions.json"


# %% [markdown]
# The registry is opened read-only by `mode=ro`. `immutable=1` is a separate promise - that the
# database cannot change while it is open, which lets SQLite skip locking and WAL recovery - and
# an immutable read of a database with an uncheckpointed write-ahead log sees the pre-WAL main
# file: a stale configuration selected silently, or a table that appears not to exist. The
# promise holds for a downloaded artifact bundle, whose tree is left unwritable, and not for a
# live case directory a sweep may be writing. `registry_readonly_uri` decides from the directory.

# %%
case_study_dir = get_case_study_dir("etfs")
registry_path = case_study_dir / "run_log" / "registry.db"
registry_hash_before = hashlib.sha256(registry_path.read_bytes()).hexdigest()

registry_uri = registry_readonly_uri(registry_path)
with sqlite3.connect(registry_uri, uri=True) as conn:
    winner = conn.execute(
        """SELECT ps.training_hash, br.backtest_hash, br.stage, bm.sharpe
           FROM backtest_runs br
           JOIN backtest_metrics bm ON br.backtest_hash = bm.backtest_hash
           JOIN prediction_sets ps ON br.prediction_hash = ps.prediction_hash
           JOIN training_runs t ON t.training_hash = ps.training_hash
           WHERE br.stage IN ('signal', 'allocation', 'risk_overlay')
             AND ps.split = 'validation'
             AND EXISTS (
                 SELECT 1 FROM prediction_sets h
                 JOIN training_runs ht ON ht.training_hash = h.training_hash
                 WHERE h.split = 'holdout'
                   AND ht.family = t.family
                   AND ht.config_name = t.config_name
                   AND ht.label = t.label
             )
           ORDER BY bm.sharpe DESC LIMIT 1"""
    ).fetchone()
    if winner is None:
        raise RuntimeError("ETF registry has no eligible cross-stage backtest winner")
    selected_family, selected_config, selected_label = conn.execute(
        "SELECT family, config_name, label FROM training_runs WHERE training_hash = ?",
        (winner[0],),
    ).fetchone()
    holdout_rows = conn.execute(
        """SELECT h.prediction_hash FROM prediction_sets h
           JOIN training_runs ht ON ht.training_hash = h.training_hash
           WHERE h.split = 'holdout'
             AND (ht.family, ht.config_name, ht.label) = (
                 SELECT t.family, t.config_name, t.label
                 FROM training_runs t WHERE t.training_hash = ?
             )
           ORDER BY h.prediction_hash""",
        (winner[0],),
    ).fetchall()

assert EXPECTED_TRAINING_HASH in (None, winner[0]), (
    f"The registry now selects a different configuration: {winner[0]}"
)
assert len(holdout_rows) == 1, f"Expected one holdout prediction set, found {len(holdout_rows)}"
prediction_hash = holdout_rows[0][0]
prediction_path = (
    case_study_dir / "run_log" / "predictions" / prediction_hash / "predictions.parquet"
)
prediction_file_hash = hashlib.sha256(prediction_path.read_bytes()).hexdigest()
assert EXPECTED_PREDICTIONS_SHA256 in (None, prediction_file_hash), (
    f"Holdout prediction set provenance changed: {prediction_file_hash}"
)
predictions = normalize_demo_predictions(pl.read_parquet(prediction_path), "symbol")
registry_hash_after_load = hashlib.sha256(registry_path.read_bytes()).hexdigest()
assert registry_hash_after_load == registry_hash_before

horizon_days = int(selected_label.removeprefix("fwd_ret_").removesuffix("d"))
REBALANCE_RULES = {5: ("weekly", "week_start"), 21: ("monthly", "month_start")}
if horizon_days not in REBALANCE_RULES:
    raise ValueError(
        f"no rebalance cadence declared for a {horizon_days}-session horizon "
        f"(label {selected_label}); add one to REBALANCE_RULES"
    )
rebalance_cadence, rebalance_date_rule = REBALANCE_RULES[horizon_days]

print(f"Registry SHA256: {registry_hash_before}")
print(f"Prediction parquet SHA256: {prediction_file_hash}")
print(f"Training hash: {winner[0]} | holdout prediction hash: {prediction_hash}")
print(f"Selection stage: {winner[2]} | backtest hash: {winner[1]}")
print(f"Selected configuration: {selected_family}/{selected_config} on {selected_label}")
print(
    f"Horizon: {horizon_days} sessions -> {rebalance_cadence} rebalance "
    f"(LEAN date_rules.{rebalance_date_rule})"
)

n_dates = predictions["timestamp"].n_unique()
n_symbols = predictions["symbol"].n_unique()

print(f"Loaded {len(predictions):,} predictions")
print(
    f"  Dates: {n_dates:,} ({predictions['timestamp'].min()} to {predictions['timestamp'].max()})"
)
print(f"  Symbols: {n_symbols}")

predictions.head(10)

# %% [markdown]
# The exported rows are the **holdout** predictions, not the validation ones, and that choice is
# the whole point of the export. Validation predictions were used to choose this configuration
# over the others, so a backtest on them measures the selection as much as the model. The holdout
# window was not read during selection, which is what makes a deployment backtest on it worth
# running. Each row is one model's out-of-sample score for one symbol on one date.
#
# Freezing those scores into a file is what separates the two loops. Thresholds, position limits
# and rebalance cadence can be changed on the platform against an unchanged prediction file, so
# a portfolio-rule experiment costs a backtest rather than a retrain.

# %% [markdown]
# ## 2. Export as QuantConnect-Compatible JSON
#
# QuantConnect's Object Store accepts JSON. We format predictions to match the
# pattern used in the
# [illustrative QuantConnect project](https://www.quantconnect.cloud/backtest/37075c225715df9ef4477dc748b1cbf7/?theme=darkly)
# (account access may be required):
# one entry per date, each containing a symbol-to-prediction mapping.

# %%
# Group by date and create QC-compatible JSON
date_groups = (
    predictions.sort("timestamp")
    .group_by("timestamp")
    .agg(
        [
            pl.col("symbol").alias("symbols"),
            pl.col("prediction").alias("predictions"),
        ]
    )
    .sort("timestamp")
)

qc_predictions = []
for row in date_groups.iter_rows(named=True):
    date_str = str(row["timestamp"])
    prediction_by_symbol = dict(zip(row["symbols"], row["predictions"], strict=False))
    qc_predictions.append(
        {
            "date": date_str,
            "prediction_by_symbol": prediction_by_symbol,
        }
    )

print(f"Formatted {len(qc_predictions)} daily prediction entries")
print(
    f"First date: {qc_predictions[0]['date']}, symbols: {len(qc_predictions[0]['prediction_by_symbol'])}"
)
print(
    f"Last date:  {qc_predictions[-1]['date']}, symbols: {len(qc_predictions[-1]['prediction_by_symbol'])}"
)

# %%
# Show sample entry
sample = qc_predictions[-1]
sample_symbols = dict(list(sample["prediction_by_symbol"].items())[:5])
print(f"\nSample entry ({sample['date']}):")
for symbol, pred in sample_symbols.items():
    direction = "long" if pred > 0 else "short" if pred < 0 else "neutral"
    print(f"  {symbol}: {pred:+.4f} ({direction})")

# %% [markdown]
# **Finding**: Each date entry maps symbols to predictions. Positive predictions
# become long candidates; the threshold determines which make it into the portfolio.

# %%
# Write to disk
json_str = json.dumps(qc_predictions, indent=2)
EXPORT_PATH.write_text(json_str)

file_size_kb = EXPORT_PATH.stat().st_size / 1024
print(f"Exported to {display_path(EXPORT_PATH)}")
print(f"  File size: {file_size_kb:.0f} KB")
print(f"  Entries: {len(qc_predictions)} dates")

# %% [markdown]
# **Finding**: The full prediction history exports to a compact JSON file. On
# QuantConnect, this would be uploaded to the Object Store via
# `qb.object_store.save('research-to-backtest-factors.json', json_str)` in a
# Research Notebook, using the same filename the LEAN algorithm reads in Section 3.
#
# **Trading implication**: Small file sizes mean fast iteration. Changing the
# threshold or adding a risk filter does not require re-uploading predictions.

# %% [markdown]
# ## 3. The LEAN Algorithm
#
# The algorithm file is deliberately tiny. All feature engineering and model
# inference happened in the research step (our pipeline). The algorithm just
# reads predictions and rebalances.
#
# This is the actual pattern from the
# [illustrative QuantConnect project](https://www.quantconnect.cloud/backtest/37075c225715df9ef4477dc748b1cbf7/?theme=darkly).

# %% [markdown]
# ### Custom Universe: Reading Predictions from Object Store
#
# The `PredictionUniverse` class defines a custom data source that reads the
# JSON we exported. LEAN streams it date-by-date into the algorithm.
#
# ```python
# class PredictionUniverse(PythonData):
#     def get_source(self, config, date, is_live_mode):
#         return SubscriptionDataSource(
#             'research-to-backtest-factors.json',
#             SubscriptionTransportMedium.OBJECT_STORE,
#             FileFormat.UNFOLDING_COLLECTION
#         )
#
#     def reader(self, config, line, date, is_live):
#         objects = []
#         for obj in json.loads(line):
#             end_time = datetime.strptime(obj["date"], "%Y-%m-%d")
#             for ticker, prediction in obj['prediction_by_symbol'].items():
#                 stock = PredictionUniverse()
#                 stock.symbol = Symbol.create(
#                     ticker, SecurityType.EQUITY, Market.USA
#                 )
#                 stock.end_time = end_time
#                 stock.value = prediction
#                 objects.append(stock)
#         return BaseDataCollection(
#             objects[-1].end_time, config.symbol, objects
#         )
# ```
#
# The `UNFOLDING_COLLECTION` format tells LEAN to stream one date at a time,
# so the algorithm only sees data available on each historical day.

# %% [markdown]
# ### Algorithm: Select and Rebalance
#
# The algorithm subscribes to assets with positive predictions and forms an
# equal-weighted portfolio. The rebalance fires on the cadence read off the
# promoted configuration's label: the signal a configuration produces is a
# forecast over a stated number of sessions, and holding a position longer than
# that means trading on a forecast that has already expired.
#
# The listing is generated from that cadence rather than written out here, so
# there is no second place for the horizon to be stated and go stale. It is the
# same reason the horizon itself is derived: this notebook previously carried a
# monthly rebalance in prose while the case study deployed a five-session
# signal, and nothing in it could fail.

# %%
ALGORITHM_TEMPLATE = """class PredictionUniverseAlgorithm(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2020, 1, 1)
        self.set_cash(100_000)
        self.settings.seed_initial_prices = True
        self._return_prediction_threshold = {threshold}

        self.universe_settings.resolution = Resolution.DAILY
        self._universe = self.add_universe(
            PredictionUniverse, self._select_assets
        )

        # Rebalance {cadence} to match the {horizon}-session prediction horizon.
        # The universe still streams daily; only the rebalance is throttled.
        self.schedule.on(
            self.date_rules.{date_rule}('SPY'),
            self.time_rules.at(8, 0),
            self._rebalance,
        )

    def _select_assets(self, data):
        return [
            stock.symbol for stock in data
            if stock.value > self._return_prediction_threshold
        ]

    def _rebalance(self):
        symbols = self._universe.selected
        if not symbols:
            return
        targets = [
            PortfolioTarget(symbol, 1 / len(symbols))
            for symbol in symbols
        ]
        self.set_holdings(targets, True)
"""

algorithm_source = ALGORITHM_TEMPLATE.format(
    threshold=PREDICTION_THRESHOLD,
    cadence=rebalance_cadence,
    horizon=horizon_days,
    date_rule=rebalance_date_rule,
)
algorithm_path = EXPORT_PATH.parent / "main.py"
algorithm_path.write_text(algorithm_source)
print(algorithm_source)
print(f"Written to {display_path(algorithm_path)}")

# %% [markdown]
# The algorithm is about thirty lines, and it ships beside the predictions so the two travel
# together. Threshold, weighting and rebalance cadence are all in it, and all can change without
# touching the model.
#
# The cadence is derived rather than declared, and that is worth noticing. It comes from the
# selected configuration's own label: a five-session forecast is rebalanced weekly so a position
# is closed before the next signal forms. Had the cadence been typed here as a constant, the
# selection moving to a different horizon would leave the algorithm trading on a schedule that no
# longer matches the signal, and nothing would say so.

# %% [markdown]
# ## 4. Running on QuantConnect
#
# ### Cloud Path (No Local Setup)
#
# An illustrative project shows the precomputed-predictions pattern:
#
# **[View Backtest Results](https://www.quantconnect.cloud/backtest/37075c225715df9ef4477dc748b1cbf7/?theme=darkly)**
#
# To use it:
# 1. Create a free QuantConnect account
# 2. Open or clone the project if your account has access
# 3. Upload your prediction JSON to the Object Store as
#    `research-to-backtest-factors.json` (the name the algorithm reads)
# 4. Run the backtest
#
# The Research Notebook in the project shows how to train a model on
# QuantConnect's own S&P 500 data and export predictions. Our export step
# above produces the same JSON format, so you can substitute your own
# predictions.
#
# ### Local Docker Path (Optional)
#
# QuantConnect documents the supported local workflow in the
# [LEAN CLI guide](https://www.quantconnect.com/docs/v2/lean-cli). The CLI uses
# Docker to initialize a project, run LEAN, and collect backtest results. Follow
# that guide for current installation and authentication commands; the export
# produced above remains the Object Store input.

# %% [markdown]
# ## 5. Prediction Signal Analysis
#
# Before deploying, verify the prediction distribution is sensible.

# %%
# Analyze the predictions we exported
positive = predictions.filter(pl.col("prediction") > PREDICTION_THRESHOLD)
negative = predictions.filter(pl.col("prediction") <= PREDICTION_THRESHOLD)

print("Prediction Distribution:")
print(f"  Total:    {len(predictions):,}")
print(f"  Long:     {len(positive):,} ({100 * len(positive) / len(predictions):.1f}%)")
print(f"  Excluded: {len(negative):,} ({100 * len(negative) / len(predictions):.1f}%)")
print(f"  Mean:     {predictions['prediction'].mean():.4f}")
print(f"  Std:      {predictions['prediction'].std():.4f}")

# Average portfolio size per date
portfolio_sizes = (
    predictions.filter(pl.col("prediction") > PREDICTION_THRESHOLD)
    .group_by("timestamp")
    .agg(pl.col("symbol").count().alias("n_holdings"))
)

print(f"\nPortfolio Size (equal-weight, threshold={PREDICTION_THRESHOLD}):")
print(f"  Mean:   {portfolio_sizes['n_holdings'].mean():.1f} holdings/day")
print(f"  Min:    {portfolio_sizes['n_holdings'].min()}")
print(f"  Max:    {portfolio_sizes['n_holdings'].max()}")

# %%
fig, ax = plt.subplots(figsize=FIGSIZE["single"])
ax.hist(
    predictions["prediction"].to_numpy(),
    bins=50,
    color=COLORS["blue"],
    edgecolor=COLORS["silver"],
    linewidth=0.4,
)
ax.axvline(PREDICTION_THRESHOLD, color=COLORS["amber"], linewidth=1.5, label="Long threshold")
ax.set(xlabel=f"Predicted {horizon_days}-day return", ylabel="Prediction count")
ax.legend(frameon=False)
add_message_title(
    ax,
    "Most predicted returns sit close to zero",
    subtitle="ETF holdout predictions at the promoted configuration's horizon; the vertical "
    "line is the long threshold",
)
show_with_alt(
    fig,
    f"Histogram of {len(predictions):,} predicted {horizon_days}-session returns, concentrated "
    f"near zero and roughly symmetric, with a vertical line at the long threshold of "
    f"{PREDICTION_THRESHOLD:g}. {len(positive):,} of them fall above it.",
)

# %%
portfolio_sizes = portfolio_sizes.sort("timestamp")
fig, ax = plt.subplots(figsize=FIGSIZE["single_wide"])
ax.plot(
    portfolio_sizes["timestamp"].to_list(),
    portfolio_sizes["n_holdings"].to_list(),
    color=COLORS["blue"],
    linewidth=1.2,
)
ax.axhline(
    portfolio_sizes["n_holdings"].mean(),
    color=COLORS["amber"],
    linewidth=1.2,
    linestyle="--",
    label="Mean breadth",
)
ax.set(xlabel="Holdout date", ylabel="Long positions")
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=4))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
ax.tick_params(axis="x", labelrotation=30)
for label in ax.get_xticklabels():
    label.set_horizontalalignment("right")
ax.legend(frameon=False)
add_message_title(
    ax,
    "Breadth swings day to day, and the threshold is what decides it",
    subtitle="Daily count of ETF predictions above the long threshold",
)
show_with_alt(
    fig,
    "Line chart of the daily count of positive predictions across the holdout window, ranging "
    f"between {portfolio_sizes['n_holdings'].min()} and "
    f"{portfolio_sizes['n_holdings'].max()} names against a dashed line at the mean of "
    f"{portfolio_sizes['n_holdings'].mean():.1f}.",
)

# %% [markdown]
# Those two figures are one argument. The histogram is the model's output and the line chart is
# the portfolio that follows from it, and the only thing between them is the threshold. Raise it
# and the second chart drops toward a handful of names on most days; lower it and the portfolio
# approaches the whole universe.
#
# That is the separation the precomputed-prediction pattern buys. The threshold is a
# portfolio-construction decision made on the platform, in code a reader can see, against a
# prediction file that does not change when it moves. Deciding it inside the model would make
# every change to it a retrain.

# %% [markdown]
# ## 6. Two Deployment Workflows Compared
#
# The precomputed-predictions pattern is one of two ways to deploy ML strategies.
# The choice depends on how often predictions change and how fast you need to
# iterate on portfolio rules.
#
# | Aspect | Precomputed Predictions | Inline Inference |
# |--------|----------------------|------------------|
# | **Pipeline** | Train offline, export JSON, then read predictions | Train offline, serialize model, then call `predict()` |
# | **Iteration speed** | Threshold/weight changes reuse frozen scores | Threshold/weight changes stay local; model changes rerun inference |
# | **Prediction freshness** | Frozen at export time | Always current |
# | **Reproducibility** | Stable input: same JSON under versioned engine rules | Depends on model and runtime versions |
# | **Best for** | Portfolio rule experimentation, walk-forward analysis | Live trading with streaming data |
# | **Book pipeline** | Chapters 7-15, export, then Ch25 QC notebook | Model serialized and loaded in `Initialize()` |
#
# Both workflows are valid. The book's pipeline naturally produces precomputed
# predictions (one run per model, label, and fold), making the export pattern
# the lower-friction path for backtesting. For live trading, QuantConnect's
# [ML documentation](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/key-concepts)
# documents the inline-inference approach with serialized models.

# %% [markdown]
# ## Summary
#
# This notebook demonstrated the prediction export bridge between the book's
# ML pipeline and QuantConnect's LEAN engine:
#
# 1. **Loaded** the output-counted ETF holdout predictions from the exact registry hash printed above
# 2. **Exported** as QC-compatible JSON (Object Store format)
# 3. **Showed** the ~30-line LEAN algorithm that consumes predictions
# 4. **Linked** to an illustrative project on QuantConnect Cloud
# 5. **Compared** precomputed vs inline deployment workflows
#
# ## Key Takeaways
#
# 1. **Separation of concerns**: The ML pipeline (Chapters 7–15) produces
#    predictions; the deployment platform (QuantConnect or self-hosted) handles
#    portfolio construction and execution. Changing one does not require
#    changing the other.
# 2. **Precomputed predictions enable fast iteration**: Testing different
#    thresholds, position limits, or rebalance rules takes seconds because
#    the expensive model training is already done.
# 3. **Platform choice is an infrastructure decision**: QuantConnect provides
#    data, execution, and hosting; the self-hosted path (`unified_framework_demo`,
#    `ml4t-backtest`) provides flexibility. The prediction format is portable
#    between both.
#
# **Next**: See `unified_framework_demo` and `etfs_deployment_loop` for the
# self-hosted path, or `pipeline_verification` for systematic parity testing.

# %%
registry_hash_final = hashlib.sha256(registry_path.read_bytes()).hexdigest()
assert registry_hash_final == registry_hash_before
print(f"Registry unchanged after export and analysis: {registry_hash_final}")

```
Exibido na íntegra, com atribuição conforme a licença da fonte. Licença: MIT
Este resumo foi escrito pelo agente de pesquisa da Stratmill com base no original; não é uma cópia da fonte.