7-10 flattener tradeIn the budget speech for FY 2023, market borrowing of 14.95 lakh crore from the market. In the Feb MPC meeting, the RBI brought down its estimates of growth and inflation potentially signaling that economy is/will go through a demand slowdown.
Now in a slowing economy, the govt. finances will be affected. Therefore, to bring back the economy on the fiscal consolidation so that sovereign bond ratings are not hit, the Indian govt. must figure out a way
1. Lower its interest payments in the face of increasing public expenditure on creating public infrastructure (read roads/highways etc. ). One simple way is to go down the yield curve in lower maturities to bring down the interest costs.
Keeping in mind (1) above, it was not difficult to expect a borrowing schedule where the shorter tenors will form a bigger percentage of the net issuance by the government.
In fact, if you look at the issuance calendar for securities below the tenor of 10 yrs (which is 2,5,7 yrs), you will find that itself comprises of ~31% of total borrowings.
Therefore, due to increased pressure on the shorter tenors and relatively less pressure on 10 yr bond yield, we can expect the yields spreads to compress in 7-10 yr region of the yield curve.
This script is written to track the same yield spread compression across 7 & 10 yr tenor.
宏觀經濟
US Treasury Constant Maturity SpreadsPlots and tabulates constant maturity treasury yield spreads
// colours per curve type for the plots and table headers
C_30Y_20Y=color.orange
C_10Y_5Y=color.purple
C_10Y_2Y=color.blue
C_7Y_5Y=color.gray
C_5Y_2Y=color.red
C_3Y_2Y=color.yellow
C_10Y_1Y=color.olive
Macroeconomic Artificial Neural Networks
This script was created by training 20 selected macroeconomic data to construct artificial neural networks on the S&P 500 index.
No technical analysis data were used.
The average error rate is 0.01.
In this respect, there is a strong relationship between the index and macroeconomic data.
Although it affects the whole world,I personally recommend using it under the following conditions: S&P 500 and related ETFs in 1W time-frame (TF = 1W SPX500USD, SP1!, SPY, SPX etc. )
Macroeconomic Parameters
Effective Federal Funds Rate (FEDFUNDS)
Initial Claims (ICSA)
Civilian Unemployment Rate (UNRATE)
10 Year Treasury Constant Maturity Rate (DGS10)
Gross Domestic Product , 1 Decimal (GDP)
Trade Weighted US Dollar Index : Major Currencies (DTWEXM)
Consumer Price Index For All Urban Consumers (CPIAUCSL)
M1 Money Stock (M1)
M2 Money Stock (M2)
2 - Year Treasury Constant Maturity Rate (DGS2)
30 Year Treasury Constant Maturity Rate (DGS30)
Industrial Production Index (INDPRO)
5-Year Treasury Constant Maturity Rate (FRED : DGS5)
Light Weight Vehicle Sales: Autos and Light Trucks (ALTSALES)
Civilian Employment Population Ratio (EMRATIO)
Capacity Utilization (TOTAL INDUSTRY) (TCU)
Average (Mean) Duration Of Unemployment (UEMPMEAN)
Manufacturing Employment Index (MAN_EMPL)
Manufacturers' New Orders (NEWORDER)
ISM Manufacturing Index (MAN : PMI)
Artificial Neural Network (ANN) Training Details :
Learning cycles: 16231
AutoSave cycles: 100
Grid
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 998
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Network
Input nodes connected: 19
Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 0
Hidden layer 3 nodes: 0
Output nodes: 1
Controls
Learning rate: 0.1000
Momentum: 0.8000 (Optimized)
Target error: 0.0100
Training error: 0.010000
NOTE : Alerts added . The red histogram represents the bear market and the green histogram represents the bull market.
Bars subject to region changes are shown as background colors. (Teal = Bull , Maroon = Bear Market )
I hope it will be useful in your studies and analysis, regards.
