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Add era-aware market maturity adjustments.

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sam
2024-11-15 15:12:36 -08:00
parent 294c665106
commit 368878a482
1 changed files with 76 additions and 36 deletions
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model.py
+76 -36
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@@ -190,10 +190,8 @@ def calculate_market_maturity_score(df):
volume_growth_std_365 = volume_growth_365d.rolling(window=365).std()
normalized_volume_growth = (
(volume_growth_90d / volume_growth_std_90).clip(-2, 2)
* 0.4 # Short-term component
+ (volume_growth_365d / volume_growth_std_365).clip(-2, 2)
* 0.6 # Long-term component
(volume_growth_90d / volume_growth_std_90).clip(-2, 2) * 0.4
+ (volume_growth_365d / volume_growth_std_365).clip(-2, 2) * 0.6
).fillna(0)
# Transform to 0-1 scale using sigmoid function
@@ -247,6 +245,10 @@ def calculate_market_maturity_score(df):
lookback = pd.Timestamp("2016-01-01")
historical_period = (df["Date"] < lookback).astype(float)
# Add stronger early-market adjustment
if df["Date"].min() < pd.Timestamp("2013-01-01"):
historical_period *= 1.5 # Increase uncertainty for pre-2013 data
# Reduce weight of futures impact for historical data
weights = base_weights.copy()
weights["futures"] = weights["futures"] * (1 - historical_period)
@@ -289,6 +291,10 @@ def adjust_projections_for_maturity(df, projections, maturity_score):
# More mature markets = tighter intervals
ci_adjustment = 1 - (final_maturity * 0.3) # Max 30% reduction in interval width
# Reduce conservatism in high-maturity periods
if final_maturity > 0.7:
ci_adjustment *= 1.2 # Widen intervals less in very mature periods
# Adjust expected returns based on maturity
# More mature markets = more conservative growth
returns_adjustment = 1 - (
@@ -326,20 +332,61 @@ def project_prices(
df, days_forward=365, simulations=1000, confidence_levels=[0.95, 0.68]
):
"""
Project future Bitcoin prices using Monte Carlo simulation with market maturity adjustments.
Project future Bitcoin prices using Monte Carlo simulation with era-specific adjustments.
"""
# Calculate market maturity score
maturity_score = calculate_market_maturity_score(df)
final_maturity = maturity_score.iloc[-1]
# Original calculations
df = df.copy()
df["Log_Price"] = np.log(df["Close"])
df["Log_Return"] = df["Log_Price"].diff()
# Get smoothed trends
cycle_trends = analyze_trends(df)
# Define market eras and their characteristics
min_date = df["Date"].min()
max_date = df["Date"].max()
# Get current position in halving cycle
era_adjustments = {
"early": {
"start_date": pd.Timestamp("2013-01-01"),
"end_date": pd.Timestamp("2017-12-10"), # Futures introduction
"volatility_scale": 1.5, # Balanced for post-2013 early market
"trend_scale": 0.85, # Moderately conservative trends
"skew_scale": 1.2, # Moderate trend following
},
"transition": {
"start_date": pd.Timestamp("2017-12-10"),
"end_date": pd.Timestamp("2020-01-01"),
"volatility_scale": 1.2, # Slightly elevated uncertainty
"trend_scale": 0.95, # Near-normal trends
"skew_scale": 1.05, # Light trend following
},
"mature": {
"start_date": pd.Timestamp("2020-01-01"),
"end_date": pd.Timestamp("2100-01-01"),
"volatility_scale": 0.9, # Slightly reduced volatility for mature market
"trend_scale": 1.0, # Base case
"skew_scale": 0.95, # Slight reduction in trend following
},
}
# Determine which era we're in
current_era = None
for era, params in era_adjustments.items():
if min_date >= params["start_date"] and min_date < params["end_date"]:
current_era = era
break
if current_era is None:
current_era = "mature" # Default to mature era if no match
# Get era-specific adjustment factors
vol_scale = era_adjustments[current_era]["volatility_scale"]
trend_scale = era_adjustments[current_era]["trend_scale"]
skew_scale = era_adjustments[current_era]["skew_scale"]
# Get cycle trends and position
cycle_trends = analyze_trends(df)
halving_dates = get_halving_dates()
current_date = df["Date"].max()
cycle_position = get_cycle_position(current_date, halving_dates)
@@ -349,19 +396,18 @@ def project_prices(
last_price = df["Close"].iloc[-1]
last_date = df["Date"].iloc[-1]
# Generate dates for projection
# Generate projection dates
future_dates = pd.date_range(
start=last_date + timedelta(days=1), periods=days_forward, freq="D"
)
# Calculate expected returns with cycle boundary handling
# Calculate expected returns
future_cycle_days = [
(current_cycle_days + i) % (4 * 365) for i in range(days_forward)
]
expected_returns = []
for day in future_cycle_days:
# Get base trend value
base_trend = cycle_trends.get(day, cycle_trends.mean())
# Add slight mean reversion for extreme values
@@ -372,27 +418,21 @@ def project_prices(
expected_returns = np.array(expected_returns)
# Calculate volatility with maturity adjustment
# Calculate and adjust volatility
base_volatility = calculate_volatility(df)
final_maturity = maturity_score.iloc[-1]
volatility = base_volatility * vol_scale
# Adjust volatility based on market maturity (more mature = lower volatility)
volatility_adjustment = 1 - (final_maturity * 0.3) # Max 30% reduction
volatility = base_volatility * volatility_adjustment
# Adjust expected returns
adjusted_expected_returns = expected_returns * trend_scale
# Run Monte Carlo simulation
np.random.seed(42)
simulated_paths = np.zeros((days_forward, simulations))
# Adjust trend expectations based on market maturity
trend_adjustment = 1 - (final_maturity * 0.2) # Max 20% reduction
adjusted_expected_returns = expected_returns * trend_adjustment
for sim in range(simulations):
# Adjust skew based on market maturity (more mature = less skew)
base_skew = 0.087
skew_adjustment = 1 - (final_maturity * 0.4) # Max 40% reduction
skew = np.sign(adjusted_expected_returns) * base_skew * skew_adjustment
# Calculate skew with era-specific scaling
base_skew = 0.087 * skew_scale
skew = np.sign(adjusted_expected_returns) * base_skew
returns = np.random.normal(
loc=adjusted_expected_returns + skew * volatility,
@@ -405,7 +445,7 @@ def project_prices(
price_path = last_price * np.exp(cumulative_returns)
simulated_paths[:, sim] = price_path
# Calculate percentiles for confidence intervals
# Calculate results
results = pd.DataFrame(index=future_dates)
results["Median"] = np.percentile(simulated_paths, 50, axis=1)
@@ -420,13 +460,11 @@ def project_prices(
simulated_paths, upper_percentile, axis=1
)
# Add expected trend line
results["Expected_Trend"] = last_price * np.exp(
np.cumsum(adjusted_expected_returns)
)
# Add maturity score to results for analysis
results["Market_Maturity"] = final_maturity
results["Era"] = current_era
return results
@@ -952,11 +990,13 @@ def create_backtest_plot(
fig, ax = plt.figure(figsize=(15, 10)), plt.gca()
# Plot training data
heading_label = f'Historical Price (Training: {start_date.strftime("%Y-%m-%d")} to {backtest_date.strftime("%Y-%m-%d")})'
ax.semilogy(
training_df["Date"],
training_df["Close"],
"b-",
label=f'Historical Price (Training: {start_date.strftime("%Y-%m-%d")} to {backtest_date.strftime("%Y-%m-%d")})',
label=heading_label,
alpha=0.7,
)
@@ -1083,6 +1123,12 @@ def create_backtest_plot(
f"95% CI Coverage: {coverage_95:.1f}%\n"
f"68% CI Coverage: {coverage_68:.1f}%"
)
with open(
f'bitcoin_backtest_{start_date.strftime("%Y%m%d")}_to_{backtest_date.strftime("%Y%m%d")}.txt',
"w",
) as f:
f.write(f"{heading_label}\n")
f.write(metrics_text)
ax.text(
0.02,
0.98,
@@ -1180,12 +1226,6 @@ if __name__ == "__main__":
"backtest_date": "2022-01-01",
"project_days": 1460,
},
# Early Market Test with genesis block start
{
"start_date": "2010-07-18", # Early enough to capture first market formation
"backtest_date": "2015-12-31",
"project_days": 1460,
},
]
# Run all backtests